Cloud resource allocation method and device, equipment and storage medium

Abstract

The invention discloses a cloud resource allocation method, device and equipment and a storage medium, and relates to the technical field of cloud computing, and the cloud resource allocation method comprises the steps: carrying out the real-time monitoring of resource use data of a target node in a cloud computing network, and generating a resource use monitoring result; determining a load prediction model matched with the target node according to the node type of the target node; through the load prediction model, according to the resource use monitoring result, performing load prediction on the target node; and determining a resource allocation demand of the target node based on a load prediction result, and performing cloud resource allocation on the target node according to the resource allocation demand. According to the technical scheme, the resource utilization rate is improved, and the expansibility and adaptability of the cloud computing network are improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a cloud resource allocation method, apparatus, device and storage medium. Background Art

[0002] With the popularization of cloud computing technology, users' demand for cloud resources has become more dynamic and diversified.

[0003] Traditional static resource allocation methods struggle to cope with complex load fluctuations, often leading to wasted resources or insufficient performance. To address this issue, dynamic cloud resource adjustment and optimization mechanisms have emerged. Their goal is to improve resource utilization and reduce resource deployment costs while maintaining service quality. Summary of the Invention

[0004] The present invention provides a cloud resource allocation method, apparatus, device and storage medium to improve the utilization of cloud resources and reduce the deployment cost of cloud resources.

[0005] According to one aspect of the present invention, a cloud resource allocation method is provided, the method comprising:

[0006] Performing real-time monitoring of resource usage data of a target node in a cloud computing network to generate resource usage monitoring results; wherein the target node is a cloud computing node in the cloud computing network to which cloud resources are to be allocated;

[0007] Determine, based on the node type of the target node, a load prediction model that matches the target node; and perform load prediction on the target node based on the resource usage monitoring result using the load prediction model; wherein the load prediction model is a pre-trained machine learning model based on historical resource usage data;

[0008] Based on the load prediction result, the resource allocation requirement of the target node is determined, and cloud resources are allocated to the target node according to the resource allocation requirement.

[0009] According to another aspect of the present invention, a cloud resource allocation device is provided, the device comprising:

[0010] A cloud resource monitoring module is used to monitor the resource usage data of a target node in a cloud computing network in real time and generate resource usage monitoring results; wherein the target node is a cloud computing node in the cloud computing network to which cloud resources are to be allocated;

[0011] A load prediction module is configured to determine a load prediction model that matches the target node based on the node type of the target node; and perform load prediction on the target node based on the resource usage monitoring result using the load prediction model; wherein the load prediction model is a pre-trained machine learning model based on historical resource usage data;

[0012] The resource allocation module is used to determine the resource allocation requirements of the target node based on the load prediction result, and to allocate cloud resources to the target node according to the resource allocation requirements.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor;

[0015] and a memory communicatively coupled to the at least one processor;

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the cloud resource allocation method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the cloud resource allocation method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the cloud resource allocation method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention monitors the real-time resource usage data of the target nodes in the cloud computing network, predicts the load of the target nodes according to the node type of the target nodes and the real-time monitoring data, and dynamically allocates resources to the target nodes according to the resource allocation requirements of the target nodes, thereby achieving flexible response to load fluctuations and improving resource utilization. At the same time, it can optimize the performance of the cloud computing network, reduce energy consumption, and improve the scalability and adaptability of the cloud computing network.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a flow chart of a cloud resource allocation method provided according to the first embodiment of the present invention;

[0023] Figure 2 This is a flow chart of a cloud resource allocation method provided according to the second embodiment of the present invention;

[0024] Figure 3 This is a structural diagram of a cloud resource allocation device provided according to Embodiment 3 of the present invention;

[0025] Figure 4 It is a structural diagram of an electronic device for implementing the cloud resource allocation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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 the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes 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.

[0028] Example 1

[0029] Figure 1A flowchart of a cloud resource allocation method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of allocating cloud resources to cloud computing nodes in a cloud computing network. The method can be executed by a cloud resource allocation device, which can be implemented in the form of hardware and / or software and can be configured in various general-purpose computing devices. Figure 1 As shown, the method includes:

[0030] S110: Monitor resource usage data of target nodes in the cloud computing network in real time and generate resource usage monitoring results.

[0031] The target node may be a cloud computing node in the cloud computing network to which cloud resources are to be allocated.

[0032] The resource usage data may include CPU (Central Processing Unit) usage, memory usage, disk IO, network bandwidth utilization, and access response time.

[0033] It should be noted that, in the embodiment of the present invention, there are various types of cloud computing nodes in the cloud computing network, such as computing nodes, storage nodes, network nodes, control nodes and other nodes in the cloud computing network.

[0034] Specifically, a lightweight monitoring agent can be deployed for each cloud computing node in the cloud computing network to monitor the resource usage data of the target node in real time. Alternatively, those skilled in the art can select the data type of resource usage data to be monitored for the cloud computing node based on the adaptability of the node type of the cloud computing node.

[0035] Optionally, the monitoring data in the resource usage monitoring results may be cleaned and preprocessed, for example, data denoising, outlier processing, and missing value interpolation may be performed on the monitoring data to improve the data quality of the monitoring data.

[0036] S120 . Determine a load prediction model that matches the target node according to the node type of the target node; and perform load prediction on the target node according to the load prediction model and resource usage monitoring results.

[0037] The load prediction model may be a pre-trained machine learning model based on historical resource usage data.

[0038] It should be noted that different types of cloud computing nodes have slightly different functions. For example, compute nodes are primarily responsible for computing tasks and require a focus on monitoring CPU usage and memory usage; storage nodes require a focus on disk I / O performance and storage capacity; and network nodes require a focus on monitoring network performance indicators such as network bandwidth, packet loss rate, and number of connections. Therefore, in embodiments of the present invention, load prediction models corresponding to different types of cloud computing nodes can be trained. Different load prediction models require different training sample data during model training. That is, the load prediction model corresponding to each node type can be trained based on historical resource usage data of the data type to be monitored corresponding to that node type.

[0039] By setting corresponding load prediction models for cloud computing nodes of different node types, the efficiency and accuracy of load prediction can be improved.

[0040] S130 . Determine the resource allocation requirements of the target node based on the load prediction result, and allocate cloud resources to the target node according to the resource allocation requirements.

[0041] Optionally, based on the load prediction result, the resource allocation requirement of the target node is determined, and cloud resources are allocated to the target node according to the resource allocation requirement, including: if the load prediction result of the target node is greater than a first preset indicator threshold of the load prediction indicator, then the resource allocation requirement of the target node is determined to be expansion; wherein the load prediction result includes the predicted values ​​of at least two load prediction indicators; if the load prediction result of the target node is less than a second preset indicator threshold of the load prediction indicator, then the resource allocation requirement of the target node is determined to be contraction; according to the resource allocation requirement, an expansion or contraction strategy is executed on the target node to perform cloud resource allocation.

[0042] It should be noted that the indicator type of the load prediction indicator corresponding to the target node can be the same as the data type of the resource usage data that the target node needs to monitor, that is, the performance indicator to be regulated. Optionally, the first preset indicator threshold and the second preset indicator threshold can be adaptively set according to those skilled in the art.

[0043] For example, if the target node is a computing node and the CPU usage index predicted in the load forecast result corresponding to the target node exceeds 70%, then the resource allocation requirement of the target node is determined to be expansion; if the CPU usage index predicted in the load forecast result corresponding to the target node is less than 30%, then the resource allocation requirement of the target node is determined to be contraction. Based on the resource allocation requirements, the control node in the cloud computing network can call the container orchestration system to dynamically allocate cloud resources to the target node, or the control node in the cloud computing network can call the node interface to start a new virtual machine instance or release an existing instance to dynamically allocate cloud resources to the target node.

[0044] Optionally, after cloud resources are allocated to a target node, the target node's load forecast indicator can be monitored to determine whether the adjusted load forecast indicator is within a preset standard threshold range. If not, the anomaly is recorded and, based on the anomaly record, the load forecast model corresponding to the target node is modified, or a cold standby resource or manual intervention strategy is activated to ensure stable operation of the target node. The standard threshold range can be adaptively set by those skilled in the art.

[0045] Optionally, in another embodiment of the present invention, if there are multiple target nodes at the same time, the node priorities of the target nodes can be used to first allocate cloud resources to the target nodes with higher node priorities, thereby achieving flexible deployment of cloud resources and ensuring service stability of the target nodes. It should be noted that the node priorities can be pre-set by those skilled in the art, or determined based on the call dependency relationship between the target nodes, and the node priority of the called node is greater than the node priority of the calling node that calls the called node.

[0046] The technical solution of the embodiment of the present invention monitors the real-time resource usage data of the target nodes in the cloud computing network, predicts the load of the target nodes according to the node type of the target nodes and the real-time monitoring data, and dynamically allocates resources to the target nodes according to the resource allocation requirements of the target nodes, thereby achieving flexible response to load fluctuations and improving resource utilization. At the same time, it can optimize the performance of the cloud computing network, reduce energy consumption, and improve the scalability and adaptability of the cloud computing network.

[0047] Example 2

[0048] Figure 2 This is a flowchart of a cloud resource allocation method provided by the second embodiment of the present invention. This embodiment further refines the above embodiment and provides specific steps for predicting the load of the target node based on the resource usage monitoring results through the load prediction model. It should be noted that for the parts not described in detail in the embodiment of the present invention, please refer to the relevant descriptions of other embodiments, which will not be repeated here. Figure 2 As shown, the method includes:

[0049] S210: If the monitoring time corresponding to the resource usage monitoring result is in the first monitoring time period, determine that the time tag of the resource usage monitoring result is the first time tag.

[0050] S220: Input the resource usage monitoring result and the first time tag into the load prediction model to determine the predicted resource usage data of the target node after the first prediction time period.

[0051] S230: If the monitoring time corresponding to the resource usage monitoring result is within the second monitoring time period, determine that the time tag of the resource usage monitoring result is the second time tag.

[0052] S240: Input the resource usage monitoring result and the second time tag into the load prediction model to determine the predicted resource usage data of the target node after the second prediction time period.

[0053] The load forecasting model includes a first load forecasting sub-model and a second load forecasting sub-model, which are respectively used to forecast the load after a first forecasting time period and a second forecasting time period; the first forecasting time period has a greater length than the second forecasting time period. Optionally, the first forecasting time period and the second forecasting time period can be adaptively set according to the knowledge of those skilled in the art.

[0054] The first time tag may be used to characterize a service peak period of the target node, and the second time tag may be used to characterize a service off-peak period of the target node.

[0055] Optionally, the first monitoring time period and the second monitoring time period can be used to characterize periods of high and low traffic volume at the target node. For example, the first monitoring time period can be a peak traffic period when traffic volume exceeds a first traffic threshold, and the second monitoring time period can be a low traffic period when traffic volume is less than a second traffic threshold. The first and second traffic volume thresholds can be adaptively set by those skilled in the art.

[0056] In an embodiment of the present invention, the first load prediction submodel can be used for long-term load prediction, and the second load prediction submodel can be used for short-term load prediction. For example, the first load prediction submodel can be used to predict the load of the target node 30 minutes later, and the second load prediction submodel can be used to predict the load of the target node 10 minutes later.

[0057] Optionally, in an embodiment of the present invention, the model structures of the first load prediction sub-model and the second load prediction sub-model may be different model structures. For example, the first load prediction sub-model may be a deep neural network model, and the second load prediction sub-model may be a time series analysis model or a machine learning model. It should be noted that the training samples of the first load prediction sub-model in the training phase are derived from the historical resource usage data of the target node within the first monitoring time period, and the training samples of the second load prediction sub-model in the training phase are derived from the historical resource usage data of the target node within the second monitoring time period.

[0058] Optionally, in another implementation of the embodiment of the present invention, cloud resources are allocated to the target node, including: prioritizing the cloud resources to be allocated according to the deployment cost of the cloud resources, and allocating cloud resources to be allocated with high priority first; wherein, the lower the deployment cost of the cloud resources to be allocated, the higher the priority of the cloud resources to be allocated.

[0059] Specifically, when the target node's load is low, the control node in the cloud computing network can identify and release the target node's idle cloud resources, such as shutting down some reserved instances or competitive bidding instances, to avoid the accumulation of ineffective costs. When it is predicted that the target node's load will increase, the control node in the cloud computing network will prioritize the deployment of cloud resources with low deployment costs and call response times within a preset time threshold, ensuring that the target node's resource configuration meets business needs while saving resource deployment costs. Optionally, the preset time threshold can be set adaptively according to those skilled in the art.

[0060] Optionally, in another implementation of the embodiment of the present invention, a resource allocation strategy for the target node can be pre-formulated based on the business needs of the target node, and cloud resources can be allocated to the target node; wherein the business needs refer to the business peak period when the business volume determined based on historical business data is greater than the first business threshold, and the business off-peak period when the business volume is less than the second business threshold.

[0061] In an embodiment of the present invention, users can flexibly set the resource management strategy of the target node according to their own business needs to achieve personalized resource allocation and adjustment. For example, cloud resource configuration can be increased for the target node in advance during business peak periods, and resources can be reduced for the target node in advance during business off-peak periods to save costs. For example, a resource allocation strategy for business peak periods (9:00-12:00 and 14:00-17:00 every day) is preset in the banking business system. The user customizes the automatic expansion of cloud resources or database instances 30 minutes before the start of the business peak period to ensure the normal operation and response of the target node during the business peak period; during the business off-peak period, the target node can be dynamically scaled down according to the actual business volume to reduce resource usage and save costs.

[0062] The technical solution of the embodiment of the present invention further improves the accuracy and efficiency of load prediction by performing time-sharing prediction on the target node according to the monitoring cycle of the resource usage monitoring result.

[0063] Example 3

[0064] Figure 3 This is a schematic diagram of the structure of a cloud resource allocation device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes:

[0065] A cloud resource monitoring module 310 is configured to monitor resource usage data of a target node in a cloud computing network in real time and generate resource usage monitoring results; wherein the target node is a cloud computing node in the cloud computing network to which cloud resources are to be allocated;

[0066] The load prediction module 320 is configured to determine a load prediction model that matches the target node based on the node type of the target node; and perform load prediction on the target node based on the load prediction model and the resource usage monitoring results; wherein the load prediction model is a pre-trained machine learning model based on historical resource usage data;

[0067] The resource allocation module 330 is configured to determine the resource allocation requirements of the target node based on the load prediction result, and to allocate cloud resources to the target node according to the resource allocation requirements.

[0068] The technical solution of the embodiment of the present invention monitors the real-time resource usage data of the target nodes in the cloud computing network, predicts the load of the target nodes according to the node type of the target nodes and the real-time monitoring data, and dynamically allocates resources to the target nodes according to the resource allocation requirements of the target nodes, thereby achieving flexible response to load fluctuations and improving resource utilization. At the same time, it can optimize the performance of the cloud computing network, reduce energy consumption, and improve the scalability and adaptability of the cloud computing network.

[0069] Optionally, the load prediction module 320 may be specifically configured to:

[0070] If the monitoring time corresponding to the resource usage monitoring result is within the first monitoring time period, determining that the time tag of the resource usage monitoring result is the first time tag;

[0071] Inputting the resource usage monitoring result and the first time tag into the load prediction model to determine predicted resource usage data of the target node after the first prediction time period;

[0072] If the monitoring time corresponding to the resource usage monitoring result is within the second monitoring time period, determining that the time tag of the resource usage monitoring result is the second time tag;

[0073] The resource usage monitoring result and the second time tag are input into the load prediction model to determine the predicted resource usage data of the target node after the second prediction time period.

[0074] Optionally, the load prediction model includes a first load prediction sub-model and a second load prediction sub-model, which are respectively used to perform load prediction after a first prediction time period and a load prediction after a second prediction time period; the period length of the first prediction time period is greater than the period length of the second prediction time period.

[0075] Optionally, the resource allocation module 330 includes:

[0076] a capacity expansion determination unit, configured to determine that the resource allocation requirement of the target node is capacity expansion if the load prediction result of the target node is greater than a first preset indicator threshold of the load prediction indicator; wherein the load prediction result includes predicted values ​​of at least two load prediction indicators;

[0077] a capacity reduction determining unit, configured to determine that the resource allocation requirement of the target node is capacity reduction if the load prediction result of the target node is less than a second preset indicator threshold of the load prediction indicator;

[0078] The resource allocation unit is used to execute a capacity expansion or contraction strategy on the target node according to the resource allocation demand to perform cloud resource allocation.

[0079] Optionally, the resource allocation module 330 further includes:

[0080] The resource priority unit is used to prioritize the cloud resources to be allocated according to their deployment costs, and allocate cloud resources with higher priorities first. The lower the deployment cost of the cloud resources to be allocated, the higher the priority of the cloud resources to be allocated.

[0081] Optionally, the device also includes a custom resource allocation module for pre-formulating a resource allocation strategy for the target node based on the business needs of the target node and allocating cloud resources to the target node; wherein the business needs refer to business peak periods when the business volume is greater than a first business threshold determined based on historical business data, and business off-peak periods when the business volume is less than a second business threshold.

[0082] The cloud resource allocation device provided in the embodiment of the present invention can execute the cloud resource allocation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0083] Example 4

[0084] Figure 4A schematic diagram of the structure of an electronic device 410 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0085] like Figure 4 As shown, the electronic device 410 includes at least one processor 411, and a memory connected to the at least one processor 411, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 411 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 to the random access memory (RAM) 413. Various programs and data required for the operation of the electronic device 410 can also be stored in the RAM 413. The processor 411, ROM 412 and RAM 413 are connected to each other via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0086] Multiple components in electronic device 410 are connected to I / O interface 415, including an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a magnetic disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows electronic device 410 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0087] Processor 411 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. Processor 411 executes the various methods and processes described above, such as the cloud resource allocation method.

[0088] In some embodiments, the cloud resource allocation method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 13 and executed by processor 411, one or more steps of the cloud resource allocation method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to execute the cloud resource allocation method in any other appropriate manner (e.g., via firmware).

[0089] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0092] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0093] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0094] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0095] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0096] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A cloud resource allocation method, characterized in that: include: Performing real-time monitoring of resource usage data of a target node in a cloud computing network to generate resource usage monitoring results; wherein the target node is a cloud computing node in the cloud computing network to which cloud resources are to be allocated; Determine, based on the node type of the target node, a load prediction model that matches the target node; and perform load prediction on the target node based on the resource usage monitoring result using the load prediction model; wherein the load prediction model is a pre-trained machine learning model based on historical resource usage data; Based on the load prediction result, the resource allocation requirement of the target node is determined, and cloud resources are allocated to the target node according to the resource allocation requirement.

2. The method according to claim 1, characterized in that The step of performing load prediction on the target node according to the resource usage monitoring result using the load prediction model includes: If the monitoring time corresponding to the resource usage monitoring result is within the first monitoring time period, determining that the time tag of the resource usage monitoring result is the first time tag; Inputting the resource usage monitoring result and the first time tag into the load prediction model to determine predicted resource usage data of the target node after the first prediction time period; If the monitoring time corresponding to the resource usage monitoring result is within the second monitoring time period, determining that the time tag of the resource usage monitoring result is the second time tag; The resource usage monitoring result and the second time tag are input into the load prediction model to determine the predicted resource usage data of the target node after the second prediction time period.

3. The method according to claim 2, characterized in that The load prediction model includes a first load prediction sub-model and a second load prediction sub-model, which are respectively used to perform load prediction after a first prediction time period and a load prediction after a second prediction time period; the period length of the first prediction time period is greater than the period length of the second prediction time period.

4. The method according to claim 1, wherein The determining the resource allocation requirement of the target node based on the load prediction result, and allocating cloud resources to the target node according to the resource allocation requirement, includes: If the load prediction result of the target node is greater than a first preset indicator threshold of the load prediction indicator, determining that the resource allocation requirement of the target node is capacity expansion; wherein the load prediction result includes predicted values ​​of at least two load prediction indicators; If the load prediction result of the target node is less than a second preset indicator threshold of the load prediction indicator, determining that the resource allocation requirement of the target node is capacity reduction; According to the resource allocation requirements, an expansion or contraction strategy is executed on the target node to perform cloud resource allocation.

5. The method according to claim 1, characterized in that The allocating cloud resources to the target node includes: Prioritize the cloud resources to be allocated according to their deployment costs, and allocate cloud resources with higher priorities first. The lower the deployment cost of the cloud resources to be allocated, the higher the priority of the cloud resources to be allocated.

6. The method according to claim 1, characterized in that Also includes: Based on the business needs of the target node, a resource allocation strategy for the target node is pre-formulated, and cloud resources are allocated to the target node; wherein the business needs refer to business peak periods when the business volume is greater than a first business threshold, and business off-peak periods when the business volume is less than a second business threshold, as determined based on historical business data.

7. A cloud resource allocation device, characterized in that: include: A cloud resource monitoring module is used to monitor the resource usage data of a target node in a cloud computing network in real time and generate resource usage monitoring results; wherein the target node is a cloud computing node in the cloud computing network to which cloud resources are to be allocated; A load prediction module is configured to determine a load prediction model that matches the target node based on the node type of the target node; and perform load prediction on the target node based on the resource usage monitoring result using the load prediction model; wherein the load prediction model is a pre-trained machine learning model based on historical resource usage data; The resource allocation module is used to determine the resource allocation requirements of the target node based on the load prediction result, and to allocate cloud resources to the target node according to the resource allocation requirements.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the cloud resource allocation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the cloud resource allocation method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The method comprises a computer program, which implements the cloud resource allocation method according to any one of claims 1 to 6 when executed by a processor.

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