Function preheating method and device for server-free architecture

Abstract

The invention discloses a function preheating method and device for a server-free architecture. The method relates to the technical field of data processing, and comprises the following steps: under the condition of detecting that a target workflow in a server-free architecture is in a starting state, determining a to-be-preheated target function from a plurality of initial functions included in the target workflow according to a function preheating strategy, the function preheating strategy is determined based on a historical execution log of the target workflow; and preheating the target function to obtain a preheating result. According to the method and the device, the technical problem of relatively low function preheating accuracy caused by performing preheating processing on all functions of the server-free architecture at regular time through a timer in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a function preheating method and device for a serverless architecture. Background Art

[0002] Serverless computing is a cloud computing execution model in which the cloud provider manages the operation of the machines, while the customer only provides the code to be run. This architecture is favored by enterprises and developers for its cost efficiency and simplified operations management.

[0003] In a serverless architecture, cold starts are a significant performance bottleneck, impacting response times and overall system efficiency. When a function is triggered for the first time or hasn't been called for a period of time, it undergoes an initialization process, which causes delays. This issue is particularly prominent in applications requiring high performance and high availability, and has become a consideration for many developers and enterprises when choosing a serverless architecture. Current solutions are mostly static, for example, periodically triggering functions to keep them "warmed up." This approach is not only resource-intensive but also lacks intelligence and cannot dynamically adjust to actual needs. Consequently, it suffers from low accuracy and wasted resources.

[0004] Currently, no effective solution has been proposed to the problem that the accuracy of function preheating is relatively low due to the timer-based preheating of all functions in the serverless architecture in the above-mentioned related technologies. Summary of the Invention

[0005] The embodiments of the present application provide a function preheating method and device for a serverless architecture, so as to at least solve the technical problem in the related art of preheating all functions of the serverless architecture by a timer, resulting in relatively low accuracy of function preheating.

[0006] According to one aspect of an embodiment of the present application, a function preheating method for a serverless architecture is provided, comprising: upon detecting that a target workflow in the serverless architecture is in a startup state, determining a target function to be preheated from a plurality of initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on a historical execution log of the target workflow; and performing preheating processing on the target function to obtain a preheating result.

[0007] Furthermore, according to the function preheating strategy, determining the target function to be preheated from the multiple initial functions included in the target workflow includes: obtaining identification information of the initial function among the multiple initial functions included in the target workflow; obtaining identification information of the function currently being executed in the target workflow; and determining the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the currently being executed function, and the function preheating conditions in the function preheating strategy.

[0008] Furthermore, before determining the target function to be preheated from the multiple initial functions included in the target workflow based on the function preheating strategy, the method also includes: obtaining the historical execution log of the target workflow; obtaining the first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information at least includes: the performance indicator data of the initial function among the multiple initial functions and the call relationship between different initial functions; and determining the function preheating strategy based on the first call data information.

[0009] Furthermore, based on the first call data information, determining the function preheating strategy includes: determining the impact degree value of the initial function on the response time of the target workflow based on the performance indicator data of the initial function among the multiple initial functions in the first call data information; determining the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; determining the function dependency relationship of the multiple initial functions based on the calling relationship between different initial functions in the first call data information; determining the function preheating strategy based on the impact degree value, the calling frequency and the function dependency relationship.

[0010] Furthermore, based on the impact value, the calling frequency and the function dependency, determining the function preheating strategy includes: generating a first function preheating condition based on the impact value and the calling frequency; generating a second function preheating condition based on the function dependency; and determining the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0011] Furthermore, the objective function is preheated to obtain a preheating result, including: waking up the objective function to obtain a processing result; allocating resources to the objective function based on the attribute information of the objective function to obtain a resource allocation result, wherein the attribute information is used to characterize the resource information required for the objective function; and obtaining the preheating result based on the processing result and the resource allocation result.

[0012] Furthermore, after preheating the objective function and obtaining a preheating result, the method also includes: if the preheating result indicates that the preheating is successful, obtaining current state information of the objective function; judging whether the objective function is in a responsive state based on the current state information; if the objective function is not in the responsive state, preheating the objective function again.

[0013] Furthermore, after preheating the target function and obtaining the preheating result, the method also includes: determining whether the target workflow has been executed; if the target workflow has been executed, obtaining the execution log of the target workflow; and optimizing the function preheating strategy based on the execution log of the target workflow.

[0014] According to one aspect of an embodiment of the present application, a function preheating method for a serverless architecture is provided, comprising: upon detecting that the serverless architecture currently satisfies a preset function preheating trigger condition, determining a target function to be preheated from multiple initial functions of the serverless architecture according to a function preheating strategy, wherein the function preheating strategy is determined based on historical call logs of multiple initial functions in the serverless architecture; performing preheating processing on the target function to obtain a preheating result.

[0015] Furthermore, before determining the target function to be preheated from the multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining historical call logs of the multiple initial functions in the serverless architecture; obtaining second call data information of the initial function among the multiple initial functions based on the historical call logs; and determining the function preheating trigger condition based on the calling time and calling condition of the initial function in the second call data information.

[0016] Furthermore, before determining the target function to be preheated from the multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining performance data information of the initial functions in the multiple initial functions and the number of calls of the initial functions in the multiple initial functions based on the historical call logs of the multiple initial functions; determining the weight value of the initial function in the multiple initial functions based on the performance data information and the number of calls; and determining the function preheating strategy based on the weight value.

[0017] According to another aspect of an embodiment of the present application, a function preheating device for a serverless architecture is also provided, including: a first determination unit, for determining a target function to be preheated from multiple initial functions included in the target workflow according to a function preheating strategy when detecting that the target workflow in the serverless architecture is in a startup state, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; a first preheating unit, for performing preheating processing on the target function to obtain a preheating result.

[0018] Furthermore, the first determination unit includes: a first acquisition module, used to obtain the identification information of the initial function among the multiple initial functions included in the target workflow; a second acquisition module, used to obtain the identification information of the function currently being executed in the target workflow; a first determination module, used to determine the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the currently executing function and the function preheating condition in the function preheating strategy.

[0019] Furthermore, the device also includes: a first acquisition unit, used to obtain the historical execution log of the target workflow before determining the target function to be preheated from the multiple initial functions included in the target workflow according to the function preheating strategy; a second acquisition unit, used to obtain the first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information at least includes: performance indicator data of the initial function among the multiple initial functions and the call relationship between different initial functions; a second determination unit, used to determine the function preheating strategy based on the first call data information.

[0020] Furthermore, the second determination unit includes: a second determination module, used to determine the impact degree value of the initial function on the response time of the target workflow based on the performance index data of the initial function among the multiple initial functions in the first call data information; a third determination module, used to determine the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; a fourth determination module, used to determine the function dependency relationship of the multiple initial functions based on the calling relationship between different initial functions in the first call data information; a fifth determination module, used to determine the function preheating strategy based on the impact degree value, the calling frequency and the function dependency relationship.

[0021] Furthermore, the fifth determination module includes: a first generation sub-module, used to generate a first function preheating condition based on the impact degree value and the call frequency; a second generation sub-module, used to generate a second function preheating condition based on the function dependency; and a determination sub-module, used to determine the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0022] Furthermore, the first preheating unit includes: an awakening module, used to perform awakening processing on the objective function to obtain a processing result; an allocation module, used to allocate resources to the objective function based on the attribute information of the objective function to obtain a resource allocation result, wherein the attribute information is used to characterize the resource information required for the objective function; a processing module, used to obtain the preheating result based on the processing result and the resource allocation result.

[0023] Furthermore, the device also includes: a third acquisition unit, which is used to obtain current state information of the objective function after preheating the objective function and obtaining a preheating result if the preheating result indicates that the preheating is successful; a first judgment unit, which is used to judge whether the objective function is in a responsive state based on the current state information; and a second preheating unit, which is used to preheat the objective function again if the objective function is not in the responsive state.

[0024] Furthermore, the device also includes: a second judgment unit, which is used to judge whether the target workflow has been executed after preheating the target function and obtaining the preheating result; a fourth acquisition unit, which is used to obtain the execution log of the target workflow if the target workflow has been executed; and an optimization unit, which is used to optimize the function preheating strategy based on the execution log of the target workflow.

[0025] According to another aspect of an embodiment of the present application, a function preheating device for a serverless architecture is also provided, including: a third determination unit, for determining a target function to be preheated from multiple initial functions of the serverless architecture according to a function preheating strategy when it is detected that the serverless architecture currently meets a preset function preheating trigger condition, wherein the function preheating strategy is determined based on historical call logs of multiple initial functions in the serverless architecture; a third preheating unit, for performing preheating processing on the target function to obtain a preheating result.

[0026] Furthermore, the device also includes: a fifth acquisition unit, used to obtain historical call logs of multiple initial functions in the serverless architecture before determining the target function to be preheated from the multiple initial functions of the serverless architecture according to the function preheating strategy; a sixth acquisition unit, used to obtain second call data information of the initial function among the multiple initial functions based on the historical call log; and a fourth determination unit, used to determine the function preheating trigger condition based on the calling time and calling condition of the initial function in the second call data information.

[0027] Furthermore, the device also includes: a seventh acquisition unit, which is used to obtain performance data information of the initial functions in the multiple initial functions and the number of calls of the initial functions in the multiple initial functions based on the historical call logs of the multiple initial functions before determining the target function to be preheated from the multiple initial functions of the serverless architecture according to the function preheating strategy; a fifth determination unit, which is used to determine the weight value of the initial function in the multiple initial functions based on the performance data information and the number of calls; and a sixth determination unit, which is used to determine the function preheating strategy based on the weight value.

[0028] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, which stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned function preheating methods for serverless architecture.

[0029] According to another aspect of an embodiment of the present invention, a computer terminal is also provided, including a memory storing an executable program; and a processor for running the program, wherein when the program is running, any one of the above-mentioned function preheating methods for a serverless architecture is executed.

[0030] According to another aspect of an embodiment of the present invention, a computer program product is also provided, which includes a stored computer program, and when the computer program is executed by a processor, it implements any one of the above-mentioned function preheating methods for serverless architecture.

[0031] In an embodiment of the present application, the following steps are adopted: upon detecting that a target workflow in a serverless architecture is in a startup state, determining a target function to be preheated from multiple initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; performing a preheating process on the target function to obtain a preheating result, thereby resolving the technical problem in the related art of preheating all functions in a serverless architecture by a timer, resulting in relatively low accuracy of function preheating. In this solution, the call and execution information of each function in the target workflow can be clearly determined through the historical execution log of the target workflow. Therefore, the function preheating strategy can be accurately determined. When the target workflow is started, the function preheating strategy can be used to more accurately determine which functions will be called in the subsequent data processing process. Compared with the static preheating strategy in the prior art, this solution can effectively improve the accuracy of function prediction through intelligent analysis of workflows and function preheating strategies. Moreover, through effective function preheating, the cold start time of serverless applications is significantly reduced, thereby achieving the effect of improving the response speed of the serverless architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 This is a hardware structure block diagram of a computer terminal provided according to the first embodiment of the present application;

[0034] Figure 2 This is the process of the function preheating method for serverless architecture provided in Example 1 of this application Figure 1 ;

[0035] Figure 3 This is the process of the function preheating method for serverless architecture provided in Example 1 of this application Figure 2 ;

[0036] Figure 4 This is the process of the function preheating method for serverless architecture provided in Example 2 of this application Figure 3 ;

[0037] Figure 5 This is the process of the function preheating method for serverless architecture provided in Example 2 of this application Figure 4 ;

[0038] Figure 6 Schematic diagram of a function preheating device for a serverless architecture provided in accordance with the third embodiment of the present application;

[0039] Figure 7 Schematic diagram of a function preheating device for a serverless architecture provided according to the fourth embodiment of the present application;

[0040] Figure 8 This is a schematic diagram of a function preheating system for a serverless architecture provided according to the fifth embodiment of the present application;

[0041] Figure 9 This is a structural block diagram of a computer terminal provided according to Example 6 of the present application. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0045] Serverless is a cloud computing model based on Platform as a Service (Paas). Serverless computing provides a micro-architecture where end customers do not need to deploy, configure, or manage server services. The server services required for code execution are all provided by the cloud platform.

[0046] Serverless Workflow: A fully managed Serverless cloud service for coordinating the execution of multiple distributed tasks. It aims to simplify the tedious tasks of developing and running task processes, including task coordination, state management, and error handling, allowing users to focus on developing task logic. Users can orchestrate distributed tasks in sequential, branching, or parallel ways. The service reliably coordinates task execution according to the predefined order, tracks each task's state transitions, and executes user-defined retry logic when necessary to ensure smooth workflow completion.

[0047] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0048] Example 1

[0049] According to an embodiment of the present application, a function preheating method for a serverless architecture is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0050] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a function preheating method for a serverless architecture is shown. Figure 1 As shown, the computer terminal (or mobile device) 10 may include a processor set 102 (the processor set 102 may include but is not limited to a processing device such as a microprocessor (Microcontroller Unit, referred to as MCU) or a programmable logic device (Field Programmer Gate Array, referred to as FPGA), and the processor set 102 may include a processor set, Figure 1102a, 102b, ..., 102n are used to illustrate), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a Universal Serial Bus (USB) port (which can be included as one of the ports of the BUS), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0051] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0052] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the function preheating method for the serverless architecture in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned function preheating method for the serverless architecture. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include but are not limited to the Internet, corporate intranet, local area network, mobile communication network and combinations thereof.

[0053] The transmission device 106 is used to receive or send data via a network. A specific example of the network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0054] The display may be a touch screen liquid crystal display that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0055] Under the above operating environment, this application provides Figure 2 The function preheating method for serverless architecture is shown. Figure 2 This is the process of the function preheating method for serverless architecture according to the first embodiment of the present application Figure 1 The function prediction method includes:

[0056] Step S201, when it is detected that the target workflow in the serverless architecture is in the startup state, a target function to be preheated is determined from multiple initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on the historical execution log of the target workflow.

[0057] Optionally, a detection tool can be used to detect whether the target workflow in the serverless architecture is in the started state, that is, to determine whether the target workflow is in the executed state. It should be noted that the workflow is a fully managed serverless cloud service used to coordinate the execution of multiple distributed tasks. Users can orchestrate distributed tasks in sequential, branching, and parallel ways. The service will reliably coordinate task execution in the set order, track the state transitions of each task, and execute user-defined retry logic when necessary to ensure the smooth completion of the workflow.

[0058] After the target workflow is in the startup state, the function that needs to be used next is predicted based on the function preheating strategy and multiple initial functions included in the target workflow, and then it is used as the target function to be preheated.

[0059] It should be noted that the function preheating strategy is determined based on the historical execution logs of the target workflow. The function preheating strategy can be used to preheat the function more accurately.

[0060] Step S202: preheat the target function to obtain a preheat result.

[0061] Optionally, the objective function is preheated to improve the efficiency of the objective function in processing data.

[0062] In summary, the historical execution logs of the target workflow can clearly identify the call and execution information of each function in the target workflow. Therefore, the function preheating strategy can be accurately determined. When the target workflow is started, the function preheating strategy can be used to more accurately determine which functions will be called in the subsequent data processing process. Compared with the static preheating strategy in the existing technology, this solution can effectively improve the accuracy of function prediction through intelligent analysis of workflows and function preheating strategies. In addition, through effective function preheating, the cold start time of serverless applications can be significantly reduced, thereby achieving the effect of improving the response speed of the serverless architecture.

[0063] In order to improve the accuracy of determining the target function, in the function preheating method for a serverless architecture provided in Example 1 of the present application, according to the function preheating strategy, determining the target function to be preheated from multiple initial functions included in the target workflow includes: obtaining identification information of the initial function among the multiple initial functions included in the target workflow; obtaining identification information of the function currently being executed in the target workflow; determining the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the function currently being executed and the function preheating conditions in the function preheating strategy.

[0064] Optionally, first clarify the multiple initial functions and identification information of the multiple initial functions included in the target workflow, then clarify the identification information of the function currently being executed in the target workflow, and finally determine the target function to be preheated based on the identification information of the initial function, the identification information of the currently being executed function, and the function preheating conditions in the function preheating strategy. It should be noted that the function preheating strategy is constructed by the preheating conditions of the trigger function. For example, a function prediction condition can be generated based on the number of times the function of the target workflow is executed. For example, if function A in the target workflow is executed 99 times out of 100 times the target workflow is executed, then function A is the above-mentioned target function; for another example, if function B is executed after function A in the target workflow, then when function A is executed, function B can be predicted.

[0065] The function preheating strategy can accurately determine the function to be preheated and improve the accuracy of function preheating.

[0066] In order to improve the accuracy of generating a function preheating strategy, in the function preheating method for a serverless architecture provided in Example 1 of the present application, before determining the target function to be preheated from multiple initial functions included in the target workflow based on the function preheating strategy, the method also includes: obtaining the historical execution log of the target workflow; obtaining the first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information includes at least: performance indicator data of the initial function among the multiple initial functions and the call relationship between different initial functions; and determining the function preheating strategy based on the first call data information.

[0067] Optionally, to improve the accuracy of generating a function warm-up strategy, the following steps are employed to obtain the aforementioned function warm-up strategy: historical execution logs of the target workflow are obtained through a logging device, and then performance indicator data of the initial functions of multiple initial functions in the target workflow and the call relationships between the different initial functions are obtained from the historical execution logs. The performance indicator data includes, but is not limited to, performance indicator data such as response time, execution time, and resource consumption of each function. Finally, the aforementioned function warm-up strategy is obtained based on the performance indicator data and the call relationships between the different initial functions.

[0068] It should be noted that the initial function among the multiple initial functions is an unused initial function and may be a partial initial function.

[0069] Performance indicator data can be used to evaluate the impact of a function on the execution time of the entire target workflow. By analyzing the impact of the execution time and the calling relationship between functions, a function preheating strategy can be derived to effectively preheat the function, thereby improving data processing efficiency.

[0070] In order to improve the accuracy of generating a function preheating strategy, in the function preheating method for a serverless architecture provided in Example 1 of the present application, determining the function preheating strategy based on the first call data information includes: determining the impact degree value of the initial function on the response time of the target workflow based on the performance indicator data of the initial function among the multiple initial functions in the first call data information; determining the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; determining the function dependency relationship of multiple initial functions based on the calling relationship between different initial functions in the first call data information; determining the function preheating strategy based on the impact degree value, calling frequency and function dependency relationship.

[0071] Optionally, determining the function preheating strategy through the first call data information includes the following steps: determining the degree of influence of the initial function on the response time of the target workflow through performance indicator data such as response time, execution time and resource consumption in the performance indicator data of the initial function among multiple initial functions; when preheating the function, targeted preheating can be performed on the functions that have a greater impact on the response time of the entire workflow, which helps to improve the response time of the workflow.

[0072] Then, the calling frequency of the initial function is calculated based on the number of times the initial function is called in the first calling data information. By identifying frequently called functions and preheating these functions, the effectiveness of function preheating can be effectively improved, avoiding the problem of resource waste.

[0073] Secondly, based on the calling relationships between the different initial functions in the first call data information, the function dependencies of the multiple initial functions are determined. This includes determining the call chain between functions, that is, how one function triggers another, and identifying data dependencies between functions, such as whether the output of one function is the input of another. This association between functions allows the next function to be executed to be pre-heated before the previous function is executed, thereby improving data processing efficiency.

[0074] Finally, the function warm-up strategy is determined based on the impact value, call frequency, and function dependencies.

[0075] The above data information can provide precise guidance for preheating functions in serverless workflows, ensuring efficient resource utilization while minimizing the impact of cold starts on performance.

[0076] Optionally, in the function preheating method for a serverless architecture provided in Example 1 of the present application, determining the function preheating strategy based on the impact value, call frequency and function dependency includes: generating a first function preheating condition based on the impact value and call frequency; generating a second function preheating condition based on the function dependency; and determining the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0077] Optionally, based on the impact value and the calling frequency, a first function preheating condition is generated. For example, functions with relatively high impact values ​​and calling frequencies are identified as functions on the critical path or frequently called functions, and then given priority preheating weights. Based on the function dependencies, a second function preheating condition is generated. For example, a preheating strategy is determined based on the call chain and dependencies between functions. For example, if function A is often called after function B, then when function B is called, function A is preheated at the same time. Finally, based on the first function preheating condition and the second function preheating condition, the function preheating strategy is determined.

[0078] Through intelligent preheating logic, the cold start delay in serverless applications can be effectively reduced while optimizing resource utilization efficiency.

[0079] In order to improve the accuracy of preheating, in the function preheating method for serverless architecture provided in Example 1 of the present application, the target function is preheated to obtain the preheating result, including: waking up the target function to obtain the processing result; allocating resources to the target function based on the attribute information of the target function to obtain the resource allocation result, wherein the attribute information is used to characterize the resource information required for the target function; and obtaining the preheating result based on the processing result and the resource allocation result.

[0080] Optionally, after preheating the objective function and obtaining a preheating result, the method further includes: if the preheating result indicates that the preheating is successful, obtaining current state information of the objective function; judging whether the objective function is in a responsive state based on the current state information; if the objective function is not in a responsive state, preheating the objective function again.

[0081] Optionally, a short initialization call is used to "wake up" the dormant function, obtain the processing results, and then allocate resources (such as memory and CPU) to the target function based on the resource information required by the target function and the expected amount of data to be processed. Finally, the warm-up result is obtained based on the processing results and resource allocation results.

[0082] After obtaining the preheating result, in order to improve the preheating accuracy, it is determined whether the preheating of the target function was successful. If the preheating result indicates that the preheating was successful, it is determined whether the target function is in a responsive state based on the current state information of the target function. It should be noted that the responsive state refers to the state in which the target function is able to process the data to be processed. If the target function is in a responsive state, it will directly wait until the data transmission process that the target function needs to process begins. If it is not in a responsive state, the target function will be preheated again.

[0083] In an optional embodiment, if the preheating operation fails, a backup solution (eg, manual processing, or no further preheating) may be used to maintain service availability.

[0084] In an optional embodiment, during preheating, resource usage is optimized to avoid unnecessary overhead. Preheating strategies can also be configured based on actual needs, such as determining whether to preheat in parallel or serially based on the relationships between functions and resource constraints. For resource-intensive functions, a gradual preheating approach can be used to prevent one-time resource usage spikes.

[0085] In an optional embodiment, during preheating, if there is a resource conflict, the preheating work on the function is stopped.

[0086] The above steps improve the accuracy of function preheating and significantly reduce the delay caused by cold starts. This not only improves system responsiveness and user experience, but also helps reduce operating costs by optimizing resource usage.

[0087] In order to further improve the accuracy of the preheating strategy, in the function preheating method for the serverless architecture provided in Example 1 of the present application, after preheating the target function and obtaining the preheating result, the method also includes: determining whether the target workflow has been executed; if the target workflow has been executed, obtaining the execution log of the target workflow; and optimizing the function preheating strategy based on the execution log of the target workflow.

[0088] Optionally, it is detected whether the target workflow has been executed and completed. If the target workflow has been executed and completed, the execution log of the target workflow is obtained so as to optimize the function preheating strategy according to the execution log.

[0089] In an optional embodiment, function performance metrics, such as startup time, execution time, and resource usage, are continuously tracked and recorded. The overall serverless system operational status, including load distribution, resource utilization, and function health, is monitored. Based on this monitoring data and performance analysis, the preheating strategy is adjusted, such as by changing the preheating frequency or the set of preheated functions. This allows the preheating strategy to automatically adapt to workflow changes, such as changes in user behavior and the deployment of new features. Real-time alerts can also be triggered when performance degradation or system anomalies are detected, enabling rapid response to avoid service interruptions.

[0090] In an optional embodiment, the following may be used: Figure 3 The flowchart shown in the figure implements function warm-up: Step 1, workflow analysis: Analyze the calling patterns and relationships of each function in the Serverless workflow, and identify the critical paths and frequently used functions.

[0091] Step 2: Intelligent preheating logic: Set the preheating strategy based on the analysis results.

[0092] Step 3: Dynamic pre-warming execution: When the workflow starts, predict the functions that are likely to be called next. Based on the predictions, dynamically pre-warm related functions. Optimize the timing and sequence of pre-warming to minimize resource consumption and response time.

[0093] Step 4: Continuous testing and adjustment: Detect actual function call patterns and adjust warm-up strategies based on the test data to improve accuracy and efficiency.

[0094] Step 1 of workflow analysis also includes: 1. Data collection. Function call logs: Collect data about when and how each function is called. Performance metrics: Measure performance metrics such as response time, execution time, and resource consumption for each function. User behavior analysis: Analyze the user behavior or external events that trigger each function call.

[0095] 2. Relationship identification and call chain analysis: Identify the call chain between functions, that is, how one function triggers another. Dependency mapping: Identify data dependencies between functions, such as when the output of one function is the input of another.

[0096] 3. Importance assessment, critical path identification: Identify the critical path in the workflow, that is, the sequence of functions that has the greatest impact on overall response time. High-frequency function identification: Identify frequently called functions that are critical to overall performance.

[0097] 4. Behavioral pattern analysis and usage pattern recognition: Analyze the temporal patterns of function usage, such as peak and trough periods. Predictive analysis: Use statistical or machine learning methods to predict which functions are likely to be triggered in specific situations.

[0098] 5. Performance bottleneck identification and latency analysis: Identify which functions’ startup or execution delays have the greatest impact on the overall workflow. Resource utilization analysis: Determine which functions consume a large amount of resources during execution.

[0099] 6. Continuous Optimization and Feedback Loop: Continuously optimize the accuracy of relationship identification and behavior pattern analysis using actual operational data. Dynamic Adjustment: Adjust the identification criteria for critical paths and high-frequency functions based on application changes and data analysis results.

[0100] The intelligent preheating logic in step 2 also includes: 1. Preheating strategy formulation, relationship-based preheating: the preheating strategy is determined based on the call chain and dependency relationship between functions. For example, if function A is often called after function B, function A is preheated at the same time when function B is called. Importance-based preheating: functions identified as being on the critical path or frequently called are given priority preheating weights. Dynamic adjustment mechanism: The preheating strategy should be able to be dynamically adjusted based on real-time detection data to cope with changes in usage patterns and workflows.

[0101] 2. Optimize the warm-up process to minimize resource consumption: Optimize the warm-up process to use the least amount of resources, for example, only warm up enough to respond to the initial request. Latency control: Ensure that the warm-up operation itself does not introduce excessive latency by controlling the execution time and order of the warm-up operations.

[0102] 3. Preheating Execution Strategy: Parallel vs. Serial Preheating: Parallel or serial preheating is determined based on the relationship between functions and resource constraints. Progressive Preheating: For resource-intensive functions, a gradual preheating approach is used to prevent one-time resource usage spikes.

[0103] 4. Continuous testing and feedback to monitor preheating effectiveness: Real-time monitoring of function performance after preheating, including startup time and response speed. Feedback loop: Feedback testing results into the preheating strategy to optimize and adjust the preheating logic.

[0104] 5. Exception handling and preheating failure handling: When preheating fails, quickly respond and take alternative measures (for example, re-preheating or stopping preheating). Resource conflict resolution: Handle resource contention issues between preheating operations and actual requests (for example, stop preheating when there is resource contention).

[0105] In a function preheating method for a serverless architecture provided in a first embodiment of the present application, upon detecting that a target workflow in a serverless architecture is in a startup state, a target function to be preheated is determined from multiple initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; the target function is preheated to obtain a preheating result, thereby solving the technical problem in the related art of preheating all functions in a serverless architecture by a timer, resulting in low accuracy of function preheating. In this solution, the call and execution information of each function in the target workflow can be clearly determined through the historical execution log of the target workflow. Therefore, the function preheating strategy can be accurately determined. When the target workflow is started, the function preheating strategy can be used to more accurately determine which functions will be called in the subsequent data processing process. Compared with the static preheating strategy in the prior art, this solution can effectively improve the accuracy of function prediction through intelligent analysis of workflows and function preheating strategies. Moreover, through effective function preheating, the cold start time of the serverless application is significantly reduced, thereby achieving the effect of improving the response speed of the serverless architecture.

[0106] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0108] Example 2

[0109] According to an embodiment of the present application, a function preheating method for a serverless architecture is also provided, such as Figure 4 As shown, the function prediction method includes:

[0110] Step S401: When it is detected that the serverless architecture currently meets a preset function preheating trigger condition, a target function to be preheated is determined from multiple initial functions of the serverless architecture according to a function preheating strategy, wherein the function preheating strategy is determined based on historical call logs of the multiple initial functions in the serverless architecture;

[0111] Optionally, a status detection device detects whether the serverless architecture currently meets a preset function preheating trigger condition. For example, the function preheating trigger condition can be a time condition or an event condition. If it is detected that the serverless architecture currently meets the preset function preheating trigger condition, a target function to be preheated is determined from multiple initial functions of the serverless architecture according to the function preheating strategy.

[0112] It should be noted that the function preheating strategy is determined based on the historical call logs of multiple initial functions in the serverless architecture. This allows for more accurate function preheating.

[0113] Step S402: preheat the target function to obtain a preheat result.

[0114] Optionally, the objective function is preheated to improve the efficiency of the objective function in processing data.

[0115] To summarize, the historical call logs of multiple initial functions in the serverless architecture can clarify the call and execution information of each function, accurately analyze the function preheating strategy, and then more accurately determine which functions will be called in the subsequent data processing process based on the function preheating strategy. This solves the technical problem in related technologies of using timers to preheat all functions in the serverless architecture, resulting in relatively low accuracy of function preheating. This improves the accuracy of function preheating and effectively improves data processing efficiency.

[0116] In order to improve the accuracy of setting the function preheating trigger condition, in the function preheating method for the serverless architecture provided in Example 2 of the present application, before determining the target function to be preheated from the multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining the historical call logs of the multiple initial functions in the serverless architecture; obtaining the second call data information of the initial function among the multiple initial functions based on the historical call logs; and determining the function preheating trigger condition based on the calling time and calling conditions of the initial function in the second call data information.

[0117] Optionally, historical call logs of multiple initial functions in the serverless architecture are obtained through a logging device. Data about when and how each function was called is then collected from the historical call logs, i.e., the aforementioned second call data information. This second call data information is analyzed to analyze the time patterns of function usage, such as peak and trough periods, as well as specific events that triggered the function (e.g., new user requests, specific data input, etc.). Finally, based on the analysis results (i.e., the aforementioned call time and call conditions), the function preheating trigger conditions are determined.

[0118] In an optional embodiment, function preheating trigger conditions may include the following two types: time-based triggering: automatically triggering the preheating logic at a specific time (such as before a peak period); event-driven triggering: triggering the preheating logic based on a specific event (such as a new user request, specific data input, etc.).

[0119] By analyzing historical call logs, we can accurately obtain the regular information of function calls, and then accurately obtain the above-mentioned function preheating trigger conditions.

[0120] In order to improve the accuracy of determining the function preheating strategy, in the function preheating method for the serverless architecture provided in Example 2 of the present application, before determining the target function to be preheated from the multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining performance data information of the initial functions in the multiple initial functions and the number of calls of the initial functions in the multiple initial functions based on the historical call logs of the multiple initial functions; determining the weight value of the initial function in the multiple initial functions based on the performance data information and the number of calls; and determining the function preheating strategy based on the weight value.

[0121] Optionally, historical call logs of multiple initial functions are obtained, and performance data information of the initial functions is obtained through the historical call logs, such as performance indicator data such as response time, execution time and resource consumption, and the number of calls of the initial functions is obtained through the historical call logs, and then key functions and frequently called functions are identified through performance indicator data and call times.

[0122] After obtaining the above performance data information and call counts, the weight value of the initial function is determined based on this data, and then a function preheating strategy is generated based on the weight value. For example, functions identified as critical functions or functions with high frequency of calls are given priority preheating weights.

[0123] In an optional embodiment, the Figure 5 The flowchart shown implements the preheating of a function: 1. Preheating trigger, automatic triggering: automatically starts the preheating process based on preset trigger conditions (such as time, event). Real-time adjustment: dynamically adjusts the preheating time and range based on real-time data and system load.

[0124] 2. Warm-up operation, function startup: "Wake up" dormant functions through a short initialization call. Resource allocation: Adjust the amount of allocated resources (such as memory and CPU) based on the characteristics and expected load of each function.

[0125] 3. Resource management and optimization: Optimize resource usage during the warm-up process to avoid unnecessary overhead. Load balancing: Ensure that warm-up operations do not burden other parts of the system and distribute resource consumption through load balancing.

[0126] 4. Status detection and status tracking: Detect the status of each function, including whether it has been preheated, current performance indicators, etc. Health check: Regularly check preheated functions to ensure they are in a responsive state.

[0127] 5. Scheduling and coordination, intelligent scheduling: intelligently arranges the execution of preheating tasks based on preheating logic and current system status. Coordination mechanism: ensures that preheating behavior is coordinated with normal workflow operations to avoid conflicts and resource competition.

[0128] 6. Performance and effectiveness evaluation: Evaluate the impact of warm-up operations on cold start time and overall performance. Feedback application: Adjust warm-up strategies and execution processes based on performance evaluation results.

[0129] 7. Exception management and error handling: When errors occur during the warm-up process, take measures to ensure stable system operation. Fallback mechanism: If the warm-up operation fails, initiate a backup plan to maintain service availability.

[0130] This series of meticulous dynamic warm-up execution steps ensures that functions in serverless applications can respond quickly when needed, significantly reducing delays caused by cold starts. This not only improves system responsiveness and user experience, but also helps reduce operating costs by optimizing resource usage.

[0131] In summary, effective function preheating can significantly reduce the cold start time of serverless applications and improve overall response speed.

[0132] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0133] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0134] Example 3

[0135] According to an embodiment of the present application, a function preheating device for a serverless architecture is provided for implementing the function preheating method for a serverless architecture in embodiment one, such as Figure 6 As shown, the device includes: a first determining unit 601 and a first preheating unit 602.

[0136] A first determining unit 601 is configured to, upon detecting that a target workflow in the serverless architecture is in a startup state, determine a target function to be preheated from a plurality of initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on a historical execution log of the target workflow;

[0137] The first preheating unit 602 is used to perform preheating processing on the target function to obtain a preheating result.

[0138] In the function preheating device for a serverless architecture provided in Example 3 of the present application, when the first determination unit 601 detects that the target workflow in the serverless architecture is in a startup state, the target function to be preheated is determined from the multiple initial functions included in the target workflow according to the function preheating strategy, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; the first preheating unit 602 preheats the target function to obtain a preheating result, which solves the technical problem in the related art that all functions of the serverless architecture are preheated by a timer at regular intervals, resulting in relatively low accuracy of function preheating. In this solution, the call and execution information of each function in the target workflow can be clearly identified through the historical execution log of the target workflow. Therefore, the function preheating strategy can be accurately determined. When the target workflow is started, the function preheating strategy can be used to more accurately determine which functions will be called in the subsequent data processing process. Compared with the static preheating strategy in the existing technology, this solution can effectively improve the accuracy of function prediction through intelligent analysis of workflows and function preheating strategies, and through effective function preheating, it can significantly reduce the cold start time of Serverless applications, thereby achieving the effect of improving the response speed of the serverless architecture.

[0139] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the first determination unit includes: a first acquisition module, used to obtain the identification information of the initial function among multiple initial functions included in the target workflow; a second acquisition module, used to obtain the identification information of the function currently being executed in the target workflow; a first determination module, used to determine the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the function currently being executed, and the function preheating conditions in the function preheating strategy.

[0140] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the device also includes: a first acquisition unit, used to obtain the historical execution log of the target workflow before determining the target function to be preheated from the multiple initial functions included in the target workflow based on the function preheating strategy; a second acquisition unit, used to obtain first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information includes at least: performance indicator data of the initial function among the multiple initial functions and the call relationship between different initial functions; a second determination unit, used to determine the function preheating strategy based on the first call data information.

[0141] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the second determination unit includes: a second determination module, used to determine the impact degree value of the initial function on the response time of the target workflow based on the performance indicator data of the initial function among the multiple initial functions in the first call data information; a third determination module, used to determine the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; a fourth determination module, used to determine the function dependency relationship of multiple initial functions based on the calling relationship between different initial functions in the first call data information; a fifth determination module, used to determine the function preheating strategy based on the impact degree value, calling frequency and function dependency relationship.

[0142] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the fifth determination module includes: a first generation sub-module, used to generate a first function preheating condition based on the impact value and the call frequency; a second generation sub-module, used to generate a second function preheating condition based on the function dependency; and a determination sub-module, used to determine the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0143] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the first preheating unit includes: a wake-up module, used to perform wake-up processing on the target function to obtain a processing result; an allocation module, used to allocate resources to the target function based on the attribute information of the target function to obtain a resource allocation result, wherein the attribute information is used to characterize the resource information required for the target function; a processing module, used to obtain a preheating result based on the processing result and the resource allocation result.

[0144] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the device also includes: a third acquisition unit, for preheating the target function and obtaining a preheating result. If the preheating result indicates that the preheating is successful, then obtain the current state information of the target function; a first judgment unit, for judging whether the target function is in a responsive state based on the current state information; and a second preheating unit, for preheating the target function again if the target function is not in a responsive state.

[0145] Optionally, in the function preheating device for a serverless architecture provided in Example 3 of the present application, the device also includes: a second judgment unit, used to judge whether the target workflow has been executed after preheating the target function and obtaining the preheating result; a fourth acquisition unit, used to obtain the execution log of the target workflow if the target workflow has been executed; and an optimization unit, used to optimize the function preheating strategy based on the execution log of the target workflow.

[0146] It should be noted that the first determination unit 601 and the first preheating unit 602 described above correspond to steps S201 to S202 in Example 1. The examples and application scenarios implemented by the two units and the corresponding steps are the same, but are not limited to the contents disclosed in Example 1. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in Example 1.

[0147] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the scheme provided in Example 1, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 1.

[0148] Example 4

[0149] According to an embodiment of the present application, a function preheating device for a serverless architecture is provided for implementing the function preheating method for a serverless architecture in embodiment 2, such as Figure 7 As shown, the device includes: a third determining unit 701 and a third preheating unit 702.

[0150] A third determining unit 701 is configured to, upon detecting that the serverless architecture currently satisfies a preset function preheating trigger condition, determine a target function to be preheated from a plurality of initial functions of the serverless architecture according to a function preheating strategy, wherein the function preheating strategy is determined based on historical call logs of the plurality of initial functions in the serverless architecture;

[0151] The third preheating unit 702 is used to perform preheating processing on the objective function to obtain a preheating result.

[0152] Optionally, in the function preheating device for a serverless architecture provided in Example 4 of the present application, the device also includes: a fifth acquisition unit, used to obtain historical call logs of multiple initial functions in the serverless architecture before determining the target function to be preheated from the multiple initial functions of the serverless architecture based on the function preheating strategy; a sixth acquisition unit, used to obtain second call data information of the initial function among the multiple initial functions based on the historical call log; and a fourth determination unit, used to determine the function preheating trigger condition based on the calling time and calling condition of the initial function in the second call data information.

[0153] Optionally, in the function preheating device for a serverless architecture provided in Example 4 of the present application, the device also includes: a seventh acquisition unit, for obtaining performance data information of an initial function and the number of calls of an initial function in a plurality of initial functions based on historical call logs of the plurality of initial functions before determining a target function to be preheated from the plurality of initial functions of the serverless architecture according to a function preheating strategy; a fifth determination unit, for determining a weight value of an initial function in a plurality of initial functions based on the performance data information and the number of calls; and a sixth determination unit, for determining a function preheating strategy based on the weight value.

[0154] It should be noted that the third determining unit 701 and the third preheating unit 702 correspond to steps S401 and S402 in the second embodiment. The examples and application scenarios implemented by the two units and the corresponding steps are the same, but are not limited to the contents disclosed in the second embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the first embodiment.

[0155] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the scheme provided in Example 2, as well as the application scenario and implementation process, but is not limited to the scheme provided in Example 2.

[0156] Example 5

[0157] According to an embodiment of the present application, a function preheating system for implementing a function preheating method for a serverless architecture is also provided. Figure 8 As shown, the function preheating system includes: an analysis engine 80, a preheating scheduler 81 and a detection system 82.

[0158] The analysis engine 80 analyzes the relationships between workflows and functions. The warmup scheduler 81 determines which functions require warmup based on the analysis engine's output. The detection system 82 tracks the actual usage of functions, providing data support for the analysis engine and warmup scheduler.

[0159] It should be noted that the preferred implementation scheme involved in the above embodiments of this application is the same as the solutions, application scenarios and implementation processes provided in Example 1 and Example 2, but is not limited to the solutions provided in Example 1 and Example 2.

[0160] Example 6

[0161] The embodiment of the present application can provide a computer terminal, which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can also be replaced by a terminal device such as a mobile terminal.

[0162] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of a computer network.

[0163] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: when it is detected that the target workflow in the serverless architecture is in the startup state, according to the function preheating strategy, determine the target function to be preheated from the multiple initial functions included in the target workflow, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; preheating the target function to obtain a preheating result.

[0164] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for a serverless architecture: according to the function preheating strategy, determining the target function to be preheated from the multiple initial functions included in the target workflow includes: obtaining the identification information of the initial function among the multiple initial functions included in the target workflow; obtaining the identification information of the function currently being executed in the target workflow; determining the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the function currently being executed and the function preheating conditions in the function preheating strategy.

[0165] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for a serverless architecture: before determining the target function to be preheated from the multiple initial functions included in the target workflow based on the function preheating strategy, the method also includes: obtaining the historical execution log of the target workflow; obtaining the first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information at least includes: performance indicator data of the initial function among the multiple initial functions and the call relationship between different initial functions; determining the function preheating strategy based on the first call data information.

[0166] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for a serverless architecture: determining the function preheating strategy based on the first call data information includes: determining the impact degree value of the initial function on the response time of the target workflow based on the performance indicator data of the initial function among the multiple initial functions in the first call data information; determining the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; determining the function dependency relationship of multiple initial functions based on the calling relationship between different initial functions in the first call data information; determining the function preheating strategy based on the impact degree value, calling frequency and function dependency relationship.

[0167] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: determining the function preheating strategy based on the impact value, call frequency and function dependency, including: generating a first function preheating condition based on the impact value and call frequency; generating a second function preheating condition based on the function dependency; determining the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0168] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: preheating the target function to obtain the preheating result including: waking up the target function to obtain the processing result; allocating resources to the target function based on the attribute information of the target function to obtain the resource allocation result, wherein the attribute information is used to characterize the resource information required by the target function; obtaining the preheating result based on the processing result and the resource allocation result.

[0169] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: after preheating the target function and obtaining the preheating result, the method also includes: if the preheating result indicates that the preheating is successful, then obtaining the current status information of the target function; based on the current status information, judging whether the target function is in a responsive state; if the target function is not in a responsive state, then preheating the target function again.

[0170] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: after preheating the target function and obtaining the preheating result, the method also includes: judging whether the target workflow has been executed; if the target workflow has been executed, obtaining the execution log of the target workflow; optimizing the function preheating strategy based on the execution log of the target workflow.

[0171] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: when it is detected that the serverless architecture currently meets the preset function preheating trigger conditions, according to the function preheating strategy, determine the target function to be preheated from multiple initial functions of the serverless architecture, wherein the function preheating strategy is determined based on the historical call logs of multiple initial functions in the serverless architecture; preheat the target function to obtain a preheating result.

[0172] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: before determining the target function to be preheated from multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining historical call logs of multiple initial functions in the serverless architecture; obtaining second call data information of the initial function among the multiple initial functions based on the historical call logs; determining the function preheating triggering condition based on the calling time and calling condition of the initial function in the second call data information.

[0173] The above-mentioned computer terminal can execute the program code of the following steps in the function preheating method for the serverless architecture: before determining the target function to be preheated from multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining performance data information of the initial functions in the multiple initial functions and the number of calls of the initial functions in the multiple initial functions based on the historical call logs of the multiple initial functions; determining the weight value of the initial function in the multiple initial functions based on the performance data information and the number of calls; and determining the function preheating strategy based on the weight value.

[0174] Optionally, Figure 9 This is a structural block diagram of a computer terminal according to an embodiment of the present application. Figure 9 As shown, the computer terminal 10 may include: one or more ( Figure 9 (only one is shown) processor 102, memory 104. The computing terminal 10 may also include a storage controller to control and manage the memory 104; the computing terminal 10 may also include a peripheral interface to connect to a radio frequency module, an audio module, and a display screen, etc.

[0175] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the function preheating method for the serverless architecture in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned function preheating method for the serverless architecture. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include but are not limited to the Internet, corporate intranet, local area network, mobile communication network and combinations thereof.

[0176] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: when it is detected that the target workflow in the serverless architecture is in the startup state, according to the function preheating strategy, determine the target function to be preheated from the multiple initial functions included in the target workflow, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; preheat the target function to obtain a preheating result.

[0177] Optionally, the processor may also execute the program code of the following steps: determining the target function to be preheated from the multiple initial functions included in the target workflow according to the function preheating strategy, including: obtaining identification information of the initial function among the multiple initial functions included in the target workflow; obtaining identification information of the function currently being executed in the target workflow; determining the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the function currently being executed, and the function preheating conditions in the function preheating strategy.

[0178] Optionally, the processor may also execute the program code of the following steps: before determining the target function to be preheated from the multiple initial functions included in the target workflow based on the function preheating strategy, the method further includes: obtaining the historical execution log of the target workflow; obtaining the first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information includes at least: the performance indicator data of the initial function among the multiple initial functions and the calling relationship between different initial functions; and determining the function preheating strategy based on the first call data information.

[0179] Optionally, the processor may also execute the program code of the following steps: determining the function preheating strategy based on the first call data information, including: determining the impact value of the initial function on the response time of the target workflow based on the performance indicator data of the initial function among the multiple initial functions in the first call data information; determining the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; determining the function dependency relationship of multiple initial functions based on the calling relationship between different initial functions in the first call data information; determining the function preheating strategy based on the impact value, calling frequency and function dependency relationship.

[0180] Optionally, the processor may also execute the program code of the following steps: determining the function preheating strategy based on the impact value, calling frequency and function dependency, including: generating a first function preheating condition based on the impact value and calling frequency; generating a second function preheating condition based on the function dependency; determining the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0181] Optionally, the above-mentioned processor can also execute the program code of the following steps: preheating the target function to obtain the preheating result including: waking up the target function to obtain the processing result; allocating resources to the target function based on the attribute information of the target function to obtain the resource allocation result, wherein the attribute information is used to characterize the resource information required by the target function; obtaining the preheating result based on the processing result and the resource allocation result.

[0182] Optionally, the processor may also execute the program code of the following steps: after preheating the target function and obtaining the preheating result, the method further includes: if the preheating result indicates that the preheating is successful, obtaining the current state information of the target function; judging whether the target function is in a responsive state based on the current state information; if the target function is not in a responsive state, preheating the target function again.

[0183] Optionally, the above-mentioned processor can also execute the program code of the following steps: after preheating the target function and obtaining the preheating result, the method also includes: judging whether the target workflow has been executed; if the target workflow has been executed, obtaining the execution log of the target workflow; optimizing the function preheating strategy based on the execution log of the target workflow.

[0184] Optionally, the processor may also execute the program code of the following steps: when it is detected that the serverless architecture currently meets the preset function preheating trigger conditions, determining the target function to be preheated from the multiple initial functions of the serverless architecture according to the function preheating strategy, wherein the function preheating strategy is determined based on the historical call logs of the multiple initial functions in the serverless architecture; performing preheating processing on the target function to obtain a preheating result.

[0185] Optionally, the processor may also execute the program code of the following steps: before determining the target function to be preheated from multiple initial functions of the serverless architecture based on the function preheating strategy, the method further includes: obtaining historical call logs of multiple initial functions in the serverless architecture; obtaining second call data information of the initial function among the multiple initial functions based on the historical call logs; and determining the function preheating trigger condition based on the calling time and calling condition of the initial function in the second call data information.

[0186] Optionally, the processor may also execute the program code of the following steps: before determining the target function to be preheated from the multiple initial functions of the serverless architecture based on the function preheating strategy, the method further includes: obtaining performance data information of the initial functions among the multiple initial functions and the number of calls of the initial functions among the multiple initial functions based on the historical call logs of the multiple initial functions; determining the weight value of the initial function among the multiple initial functions based on the performance data information and the number of calls; and determining the function preheating strategy based on the weight value.

[0187] It can be understood by those skilled in the art that Figure 9 The structure shown is for illustration only, and the computer terminal may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 9 It does not limit the structure of the above electronic device. For example, the computer terminal 10 may also include Figure 9 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 9 Different configurations shown.

[0188] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0189] Example 7

[0190] The embodiment of the present application further provides a computer-readable storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the function preheating method for the serverless architecture provided in the first embodiment.

[0191] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0192] Optionally, in this embodiment, the above-mentioned storage medium is configured to store program code for executing the following steps: when it is detected that the target workflow in the serverless architecture is in the startup state, according to the function preheating strategy, determine the target function to be preheated from the multiple initial functions included in the target workflow, wherein the function preheating strategy is determined based on the historical execution log of the target workflow; preheating the target function to obtain a preheating result.

[0193] Furthermore, according to the function preheating strategy, determining the target function to be preheated from the multiple initial functions included in the target workflow includes: obtaining identification information of the initial function among the multiple initial functions included in the target workflow; obtaining identification information of the function currently being executed in the target workflow; and determining the target function to be preheated based on the identification information of the initial function among the multiple initial functions, the identification information of the function currently being executed, and the function preheating conditions in the function preheating strategy.

[0194] The above-mentioned storage medium is configured to store program code for executing the following steps: before determining the target function to be preheated from multiple initial functions included in the target workflow based on the function preheating strategy, the method also includes: obtaining the historical execution log of the target workflow; obtaining the first call data information of the initial function among the multiple initial functions based on the historical execution log, wherein the first call data information at least includes: performance indicator data of the initial function among the multiple initial functions and the calling relationship between different initial functions; determining the function preheating strategy based on the first call data information.

[0195] The above-mentioned storage medium is configured to store program code for executing the following steps: determining the function preheating strategy based on the first call data information, including: determining the impact degree value of the initial function on the response time of the target workflow based on the performance indicator data of the initial function among the multiple initial functions in the first call data information; determining the calling frequency of the initial function based on the number of times the initial function in the first call data information is called; determining the function dependency relationship of multiple initial functions based on the calling relationship between different initial functions in the first call data information; determining the function preheating strategy based on the impact degree value, calling frequency and function dependency relationship.

[0196] The above-mentioned storage medium is configured to store program code for executing the following steps: determining the function preheating strategy based on the impact value, call frequency and function dependency, including: generating a first function preheating condition based on the impact value and call frequency; generating a second function preheating condition based on the function dependency; determining the function preheating strategy based on the first function preheating condition and the second function preheating condition.

[0197] The above-mentioned storage medium is configured to store program codes for executing the following steps: preheating the target function to obtain a preheating result including: waking up the target function to obtain a processing result; allocating resources to the target function based on the attribute information of the target function to obtain a resource allocation result, wherein the attribute information is used to characterize the resource information required for the target function; and obtaining a preheating result based on the processing result and the resource allocation result.

[0198] The above-mentioned storage medium is configured to store program code for executing the following steps: after preheating the target function and obtaining a preheating result, the method also includes: if the preheating result indicates that the preheating is successful, obtaining the current state information of the target function; based on the current state information, judging whether the target function is in a responsive state; if the target function is not in a responsive state, preheating the target function again.

[0199] The above-mentioned storage medium is configured to store program code for executing the following steps: after preheating the target function and obtaining the preheating result, the method also includes: determining whether the target workflow has been executed; if the target workflow has been executed, obtaining the execution log of the target workflow; and optimizing the function preheating strategy based on the execution log of the target workflow.

[0200] The above-mentioned storage medium is configured to store program code for executing the following steps: when it is detected that the serverless architecture currently meets the preset function preheating trigger condition, according to the function preheating strategy, determine the target function to be preheated from multiple initial functions of the serverless architecture, wherein the function preheating strategy is determined based on the historical call logs of multiple initial functions in the serverless architecture; preheat the target function to obtain a preheating result.

[0201] The above-mentioned storage medium is configured to store program code for executing the following steps: before determining the target function to be preheated from multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining historical call logs of multiple initial functions in the serverless architecture; obtaining second call data information of the initial function among the multiple initial functions based on the historical call logs; determining the function preheating trigger condition based on the calling time and calling condition of the initial function in the second call data information.

[0202] The above-mentioned storage medium is configured to store program code for executing the following steps: before determining the target function to be preheated from multiple initial functions of the serverless architecture based on the function preheating strategy, the method also includes: obtaining performance data information of the initial functions in the multiple initial functions and the number of calls of the initial functions in the multiple initial functions based on the historical call logs of the multiple initial functions; determining the weight value of the initial function in the multiple initial functions based on the performance data information and the number of calls; and determining the function preheating strategy based on the weight value.

[0203] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0204] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0205] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0206] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0207] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0208] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0209] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A function preheating method for a serverless architecture, characterized in that: include: When detecting that a target workflow in a serverless architecture is in a startup state, determining a target function to be preheated from a plurality of initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on a historical execution log of the target workflow; The objective function is preheated to obtain a preheating result.

2. The method according to claim 1, characterized in that According to the function preheating strategy, determining the target function to be preheated from the multiple initial functions included in the target workflow includes: Obtaining identification information of an initial function among a plurality of initial functions included in the target workflow; Obtaining identification information of a function currently being executed in the target workflow; The target function to be preheated is determined according to identification information of an initial function among the multiple initial functions, identification information of the currently executed function, and a function preheating condition in the function preheating strategy.

3. The method according to claim 1, characterized in that Before determining a target function to be preheated from a plurality of initial functions included in the target workflow according to the function preheating strategy, the method further includes: Obtaining the historical execution log of the target workflow; Obtaining, based on the historical execution log, first call data information of an initial function among the multiple initial functions, wherein the first call data information at least includes: performance indicator data of the initial function among the multiple initial functions and call relationships between different initial functions; Determine the function preheating strategy based on the first call data information.

4. The method according to claim 3, characterized in that Determining the function preheating strategy according to the first call data information includes: Determining, based on performance indicator data of an initial function among the multiple initial functions in the first call data information, an impact degree value of the initial function on the response time of the target workflow; Determining a calling frequency of the initial function according to the number of times the initial function in the first calling data information is called; Determining a function dependency relationship among the plurality of initial functions based on a calling relationship between different initial functions in the first calling data information; The function preheating strategy is determined according to the impact degree value, the call frequency, and the function dependency.

5. The method according to claim 4, characterized in that Determining the function warm-up strategy according to the impact value, the call frequency, and the function dependency includes: generating a first function preheating condition according to the impact degree value and the call frequency; Generating a second function preheating condition according to the function dependency; The function preheating strategy is determined according to the first function preheating condition and the second function preheating condition.

6. The method according to claim 1, wherein The objective function is preheated to obtain a preheating result including: Performing a wake-up process on the target function to obtain a processing result; Allocating resources to the objective function according to the attribute information of the objective function to obtain a resource allocation result, wherein the attribute information is used to represent resource information required by the objective function; The preheating result is obtained according to the processing result and the resource allocation result.

7. The method according to claim 1, characterized in that After preheating the objective function to obtain a preheating result, the method further includes: If the preheating result indicates that the preheating is successful, obtaining current state information of the objective function; Determining whether the target function is in a responsive state according to the current state information; If the objective function is not in the responsive state, the objective function is preheated again.

8. The method according to claim 1, characterized in that After preheating the objective function to obtain a preheating result, the method further includes: Determine whether the target workflow has been completed; If the target workflow has been executed, obtaining the execution log of the target workflow; The function preheating strategy is optimized according to the execution log of the target workflow.

9. A function preheating method for a serverless architecture, characterized in that: include: When it is detected that the serverless architecture currently meets a preset function preheating trigger condition, determining a target function to be preheated from multiple initial functions of the serverless architecture according to a function preheating strategy, wherein the function preheating strategy is determined based on historical call logs of the multiple initial functions in the serverless architecture; The objective function is preheated to obtain a preheating result.

10. The method according to claim 9, characterized in that Before determining a target function to be preheated from a plurality of initial functions of the serverless architecture according to the function preheating strategy, the method further includes: Obtain historical call logs of multiple initial functions in the serverless architecture; Acquire second call data information of an initial function among the multiple initial functions according to the historical call log; The function preheating triggering condition is determined according to the calling time and calling condition of the initial function in the second calling data information.

11. The method according to claim 9, characterized in that Before determining a target function to be preheated from a plurality of initial functions of the serverless architecture according to the function preheating strategy, the method further includes: Obtaining, based on historical call logs of the multiple initial functions, performance data information of the initial functions among the multiple initial functions and the number of calls of the initial functions among the multiple initial functions; Determining a weight value of an initial function among the multiple initial functions based on the performance data information and the number of calls; The function preheating strategy is determined according to the weight value.

12. A function preheating device for serverless architecture, characterized in that: include: a first determining unit configured to, upon detecting that a target workflow in the serverless architecture is in a startup state, determine a target function to be preheated from a plurality of initial functions included in the target workflow according to a function preheating strategy, wherein the function preheating strategy is determined based on a historical execution log of the target workflow; The first preheating unit is used to perform preheating processing on the objective function to obtain a preheating result.

13. A function preheating device for serverless architecture, characterized in that: include: a third determining unit, configured to, upon detecting that the serverless architecture currently satisfies a preset function preheating trigger condition, determine a target function to be preheated from a plurality of initial functions of the serverless architecture according to a function preheating strategy, wherein the function preheating strategy is determined based on historical call logs of the plurality of initial functions in the serverless architecture; The third preheating unit is used to perform preheating processing on the objective function to obtain a preheating result.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the function preheating method for serverless architecture described in any one of claims 1 to 11.

15. A computer terminal, characterized in that: include: a memory storing an executable program; A processor is used to run the program, wherein when the program is running, it executes the function preheating method for serverless architecture described in any one of claims 1 to 11.

16. A computer program product, characterized in that The computer program product includes a stored computer program, and when the computer program is executed by a processor, the function preheating method for a serverless architecture described in any one of claims 1 to 11 is implemented.

Images