Serverless function asynchronous loading optimization system and method

Abstract

The invention provides a Serverless function asynchronous loading optimization system and method, and aims to solve the problem of long cold start time of a Serverless function. The system comprises an asynchronous loading configurator, a component dependency analyzer, a priority loading scheduler and a partial function availability controller. The system generates an accurate component dependency graph by analyzing a function structure and a strategy defined by a developer, and realizes priority loading according to the component dependency graph. Innovative partial function availability control enables a function to respond to a specific request before complete loading. The method has the core advantages that the fine-grained component level control and loading strategy is realized, the cold start time of the Serverless function is remarkably shortened, the resource utilization efficiency and the function availability are improved, and a new performance optimization scheme is provided for Serverless calculation.

Description

Technical Field

[0001] The present invention relates to the fields of cloud computing and function as a service (FaaS), and in particular to performance optimization technology for Serverless functions. Specifically, the present invention provides an asynchronous loading system and method for optimizing the function loading process and resource utilization efficiency in response to the cold start problem of Serverless functions. Background Art

[0002] As a new paradigm of cloud computing, serverless computing has received widespread attention and application in recent years. It allows developers to focus on writing and deploying a single function without having to manage the underlying infrastructure, and has important applications in software development, data processing, the Internet of Things, and other fields. Serverless architecture provides enterprises with greater flexibility and cost-effectiveness through features such as on-demand resource allocation, automatic expansion, and billing based on usage.

[0003] Currently, major serverless platforms all use similar function loading and execution mechanisms. When a function is called, the platform first needs to prepare the execution environment, load the function code and its dependencies, and then execute the function. This process is called a "cold start". In order to optimize performance, these platforms usually keep the function instance active for a certain period of time to quickly respond to subsequent requests, which is called a "hot start".

[0004] However, this solution has significant cold start latency issues, especially for complex functions or functions that need to load large dependencies (such as machine learning models). Cold start latency can range from hundreds of milliseconds to several seconds, seriously affecting user experience and certain latency-sensitive application scenarios. In addition, in order to reduce cold starts, keeping function instances active increases resource consumption and costs, which contradicts the on-demand usage concept of serverless.

[0005] To solve the above problems, the industry has proposed a variety of optimization strategies, such as preheating function instances, optimizing the size of dependency packages, and using lightweight runtimes. Although these methods have improved the cold start problem to a certain extent, they still have limitations. For example, preheating strategies increase costs, while optimizing dependency packages may limit the functionality of functions. In addition, these methods are usually general and difficult to fine-tune for the specific needs of different functions.

[0006] Therefore, there is an urgent need for a new solution that can manage the function loading process, balance performance and resource consumption, and adapt to different function requirements. Developing a serverless function optimization system that can achieve fine-grained control and support asynchronous loading has become a research hotspot in this field. Such a system can not only significantly reduce the cold start time, but also improve resource utilization efficiency, providing important support for the further development of serverless computing. Summary of the invention

[0007] The main purpose of the present invention is to solve the problems of long cold start time and low resource utilization efficiency of Serverless functions. In addition, the present invention also aims to provide a flexible and configurable loading optimization solution to meet the specific needs of different types of Serverless functions.

[0008] To achieve the above objectives, the present invention provides a Serverless function asynchronous loading optimization system and method, which is characterized by: including a user-defined asynchronous loading configurator, a component dependency analyzer, a priority-based loading scheduler, and a partial function availability controller. In some embodiments, the system also includes a resource monitoring module for dynamically adjusting resource allocation.

[0009] Specifically, the user-defined asynchronous loading configurator is used to receive and process the asynchronous loading strategy of the module or function specified by the developer, and supports definition through configuration files or code annotations. The configurator includes a configuration file parsing module for parsing the standard format configuration file (such as YAML or JSON format) provided by the developer; a code annotation recognition module for identifying asynchronous loading annotations in function codes; and a configuration verification module for verifying the validity and consistency of the user-defined configuration.

[0010] Furthermore, the component dependency analyzer generates a component dependency graph based on the asynchronous loading strategy and function code structure, and identifies module imports and function call relationships through static code analysis. Specifically, it includes a static code analysis module for analyzing the abstract syntax tree of the function source code; a configuration integration module for combining the user-defined asynchronous loading configuration with the static analysis results; and a dependency graph optimization module for generating an optimized loading order based on the analysis results.

[0011] Preferably, the priority-based loading scheduler schedules the loading order of components according to the component dependency graph and the user-defined priority, and implements a configurable concurrent loading mechanism. The scheduler includes a priority queue management module for managing the priority queues of components to be loaded; a concurrency control module for implementing a configurable concurrent loading mechanism; and an error handling module for implementing a retry and downgrade strategy for components that fail to load.

[0012] Optionally, the partial function availability controller manages the partial availability status of the function, enabling the function to respond to specific requests before it is fully loaded, including status tracking, request filtering, and downgrade strategy execution. Specifically, it includes a status tracking module for maintaining the loading status of each function module; a request filtering module for determining which requests can be processed immediately based on the currently available functions; and a downgrade strategy execution module for implementing user-defined downgrade or waiting strategies for requests that depend on unloaded components.

[0013] In one embodiment, the system further includes an error handling module for implementing a retry and downgrade strategy for components that fail to load.

[0014] In some embodiments, the user-defined asynchronous loading configurator includes a configuration file parsing module, a code annotation identification module, and a configuration verification module to ensure that the user-defined configuration is valid and consistent.

[0015] In addition, the present invention can also implement a resource monitoring module for real-time monitoring of resource usage of each component and dynamically adjusting resource allocation according to the monitoring results.

[0016] In other embodiments, the system can integrate machine learning models to predict function call patterns based on historical data and further optimize the loading strategy.

[0017] In a preferred embodiment, the system first receives the asynchronous loading strategy defined by the developer through configuration files or code annotations, and then uses static code analysis to generate a component dependency graph. Based on this dependency graph and priority, the system schedules the component loading order and enables some function availability control. During the loading process, the system continuously monitors resource usage, dynamically adjusts resource allocation, and handles possible loading errors.

[0018] In another preferred embodiment, the system can be integrated with the existing Serverless platform as a middle layer to optimize the loading and execution process of functions without modifying the underlying platform architecture.

[0019] By adopting the above scheme, the present invention has the following beneficial effects: 1. Significantly reduce the cold start time of Serverless functions and improve response speed; 2. Implement fine-grained component-level control to optimize resource utilization efficiency; 3. Provide flexible user configuration options to meet the specific needs of different types of functions; 4. Improve the overall availability of functions through partial function availability control; 5. Dynamic resource allocation and error handling mechanisms enhance the stability and reliability of the system.

[0020] In summary, the present invention provides an innovative Serverless function loading optimization solution, which not only effectively solves the cold start problem, but also improves resource utilization efficiency and system flexibility, providing important support for the further development and application of Serverless computing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is the architecture diagram of the Serverless function intelligent asynchronous loading optimization system.

[0023] Figure 2 It is the structure diagram of asynchronous loading configurator.

[0024] Figure 3 This is a diagram of the component dependency analyzer workflow.

[0025] Figure 4 It is the state flow diagram of the priority loading scheduler.

[0026] Figure 5 It is a partial function availability controller request processing flow chart.

[0027] Figure 6 It is the structure diagram of the resource monitoring and dynamic adjustment module.

[0028] Figure 7 It is an overview of the cross-platform adaptation and performance optimization mechanism.

[0029] Figure 8 It is a structural diagram of the intelligent cache and state management mechanism. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0032] Example 1: Basic system architecture.

[0033] The basic architecture of the Serverless function asynchronous loading optimization system provided by the present invention is as follows: Figure 1 As shown in the figure, the system mainly includes four core components: asynchronous loading configurator 10, component dependency analyzer 20, priority loading scheduler 30 and partial function availability controller 40. These components work together to achieve asynchronous loading optimization of Serverless functions.

[0034] The asynchronous loading configurator 10 is responsible for receiving and processing the asynchronous loading strategy defined by the developer. The configurator supports multiple configuration methods, including but not limited to configuration files in YAML or JSON format, and using specific annotations directly in the function code. The core innovation of the configurator lies in its flexibility and scalability, allowing developers to finely control the loading process according to the characteristics and requirements of specific functions.

[0035] The component dependency analyzer 20 is one of the key innovations of this system. It generates an accurate component dependency graph through static code analysis technology combined with user-defined configuration. This dependency graph not only contains the dependencies between the components of the function, but also the estimated loading time and resource requirements of each component. The analyzer uses an improved abstract syntax tree (AST) analysis algorithm to accurately identify complex module imports and function call relationships. For example, for a typical machine learning function, the analyzer can identify the dependencies between key components such as model loading, data preprocessing, and reasoning, and assign appropriate loading priorities to each component.

[0036] The priority loading scheduler 30 implements component loading scheduling based on the dependency graph generated by the component dependency analyzer. Its core innovation lies in the use of a dynamic priority queue algorithm that can dynamically adjust the loading order according to system load and resource availability at runtime. The scheduler also implements a configurable concurrent loading mechanism, which allows users to adjust the degree of concurrency according to specific scenarios in a parameterized manner. For example, when resources are sufficient, the scheduler may load multiple independent components at the same time to reduce the overall loading time; when resources are limited, it will prioritize loading components on the critical path.

[0037] The partial function availability controller 40 is another important innovation of the system. It allows functions to respond to partial requests before they are fully loaded, significantly improving the availability and response speed of functions. The controller maintains a dynamic function availability state graph that accurately tracks the loading status of each function module. When a request is received, the controller decides whether it can process the request based on the currently loaded function modules, or executes a graceful degradation strategy.

[0038] The collaborative workflow of these four core components is as follows: 1. When the developer deploys a function, the asynchronous loading configurator first parses the configuration information provided by the user. 2. The component dependency analyzer then analyzes the function code and combines the configuration information to generate a detailed component dependency graph. 3. The priority load scheduler makes an initial load plan based on the dependency graph. 4. When the function is called for the first time, the scheduler starts executing the load plan and the partial function availability controller starts tracking the load status. 5. During the loading process, if a function call request is received, the controller will check whether the function required by the request is already available. If it is available, the request will be processed immediately; if it is not available, a waiting or downgrade operation will be performed according to a predetermined strategy. 6. The scheduler continuously optimizes the loading order to maximize the availability and performance of the function.

[0039] A key advantage of this system is its adaptability and scalability. For example, it can easily integrate additional optimization modules, such as a resource monitoring module (not shown in the figure), which is used to monitor resource usage in real time and dynamically adjust the loading strategy. The system also reserves interfaces for machine learning model integration, which can be used to predict function call patterns and further optimize loading strategies.

[0040] The system of the present invention effectively reduces the cold start time of Serverless functions and improves resource utilization efficiency through asynchronous loading and partial function availability control. In particular, for functions containing large dependencies (such as machine learning models), the performance improvement is more significant. However, the specific degree of performance improvement may vary depending on the application scenario and specific implementation. It is recommended to perform detailed performance testing and optimization in actual deployment.

[0041] It should be noted that the above embodiment is only one of the preferred implementations of the present invention and is not intended to limit the protection scope of the present invention. Those skilled in the art can adjust and expand the system architecture according to actual needs, such as adding more optimization modules or adjusting the interaction mode between components, and these changes should be considered to fall within the protection scope of the present invention.

[0042] Example 2: Asynchronously loading a configurator.

[0043] This embodiment describes in detail the structure and working principle of the asynchronous loading configurator, which is one of the core components of the system of the present invention. Figure 2 The asynchronous loading configurator mainly includes three modules: a configuration file parsing module 11, a code annotation recognition module 12 and a configuration verification module 13.

[0044] The main function of the configuration file parsing module 11 is to parse the standard format configuration file provided by the developer. The present invention supports a variety of commonly used configuration file formats, including but not limited to YAML and JSON. This flexibility allows developers to choose the format that best suits their development environment and habits. The structure of the configuration file is as follows:

[0045] For YAML format: functions: -name:mainFunction asyncComponents: -name:heavyModel priority:high loadCondition:"onDemand" -name:dataPreprocessor priority:medium loadCondition:"preload"

[0046] For JSON format: { "functions":[ { "name":"mainFunction", "asyncComponents":[ { "name":"heavyModel", "priority":"high", "loadCondition":"onDemand" }, { "name":"dataPreprocessor", "priority":"medium", "loadCondition":"preload" } ] } ] }

[0047] The configuration file parsing module 11 uses a recursive descent parsing algorithm to efficiently process complex nested structures. The innovation of this module lies in its adaptive parsing mechanism, which can dynamically adjust the parsing strategy according to the complexity of the configuration file to ensure high efficiency when processing large-scale configurations.

[0048] The code annotation recognition module 12 is responsible for identifying specific annotations in the function source code. These annotations allow developers to define asynchronous loading strategies directly in the code, providing finer-grained control than configuration files. The syntax of the annotation is as follows: @AsyncLoad(priority="high",loadCondition="onDemand") function heavyModel(){ / / Model loading logic }

[0049] The code annotation recognition module 12 uses an improved abstract syntax tree (AST) traversal algorithm to accurately identify and extract these annotation information. An important innovation of this module is its context-awareness capability, which can understand the code structure where the annotation is located, thereby providing more accurate loading strategy suggestions.

[0050] The configuration verification module 13 is key to ensuring system stability and consistency. It performs the following verifications: 1. Syntax verification: Check the syntax correctness of configuration files and annotations. 2. Semantic verification: Ensure that the configuration is semantically reasonable, such as consistency in priority settings. 3. Conflict detection: Identify and resolve potential conflicts between configuration files and code annotations. 4. Dependency check: Verify whether the dependencies between asynchronously loaded components are reasonable.

[0051] The configuration verification module 13 uses a rule-based verification engine combined with a graph theory algorithm to detect circular dependencies. Its innovation lies in the use of a fuzzy logic algorithm that can handle fuzzy matching and conflict resolution between different configuration sources.

[0052] The workflow of asynchronous loading configurator is as follows: 1. When a function is deployed, the configuration file parsing module 11 first parses the configuration file. 2. At the same time, the code annotation recognition module 12 analyzes the function source code and extracts the annotation information. 3. Configuration Verification Module 13 then performs a comprehensive verification on all collected configuration information. 4. If any problems are found, the system will generate a detailed error report to guide developers to make corrections. 5. After verification, the configuration information is converted into a standardized internal representation for use by other components of the system.

[0053] A key advantage of this embodiment is its high extensibility. For example, it is easy to add support for new configuration file formats or extend the functionality of annotations. In addition, the configuration validation module can integrate machine learning algorithms to provide more accurate validation and suggestions by learning historical configuration patterns.

[0054] It should be noted that the performance of asynchronously loading configurators has a direct impact on the startup time of the entire system. Therefore, in actual deployment, it is recommended to perform performance tests on large-scale configuration files and complex functions, and adjust the parsing and verification strategies as needed.

[0055] The asynchronous loading configurator described in this embodiment provides a flexible and powerful configuration mechanism for asynchronous loading optimization of Serverless functions. It not only supports multiple configuration methods, but also ensures the correctness and consistency of the configuration through strict verification. This approach significantly improves the availability and maintainability of the system, allowing developers to more finely control the loading behavior of functions, thereby optimizing performance and resource utilization.

[0056] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art can adjust and expand the configurator according to specific needs, such as adding new configuration format support, or implementing more complex verification logic. These changes and modifications should be considered to fall within the scope of protection of the present invention.

[0057] Embodiment 3: Component dependency analyzer.

[0058] This embodiment describes in detail the structure and working principle of the component dependency analyzer, which is one of the core innovative components of the system of the present invention. Figure 3 The component dependency analyzer mainly includes three modules: a static code analysis module 21, a configuration integration module 22 and a dependency graph optimization module 23.

[0059] The main function of the static code analysis module 21 is to analyze the abstract syntax tree (AST) of the function source code to identify module imports and function call relationships. This module uses an improved AST traversal algorithm, and its innovation lies in its ability to handle complex asynchronous programming patterns and dynamic imports. The static code analysis module performs steps such as source code parsing, AST traversal, symbol table construction, and cross-file analysis to build a complete dependency relationship. A key innovation of this module is its incremental analysis capability. When a function code is partially modified, it can only analyze the changed part, greatly improving the analysis efficiency.

[0060] The configuration integration module 22 is responsible for combining the user-defined asynchronous loading configuration with the static analysis results. This process involves key steps such as configuration mapping, priority fusion, and conflict resolution. The innovation of the configuration integration module 22 is that it uses a fuzzy logic algorithm to handle the incomplete match between the configuration and the actual structure of the code. This method greatly improves the robustness of the system and can handle various non-standard or incomplete configurations.

[0061] The dependency graph optimization module 23 is the core of this embodiment and is responsible for generating the final optimized dependency graph based on the output of the first two modules. This module uses an improved graph algorithm that can effectively handle large-scale and complex dependencies. The main steps include initial graph construction, circular dependency detection, critical path analysis, and parallel loading optimization. An important innovation of the dependency graph optimization module 23 is the introduction of a dynamic weight adjustment mechanism. This mechanism can dynamically adjust the weights of each edge in the dependency graph according to the actual call pattern of the function, thereby achieving adaptive optimization of the loading order.

[0062] The overall workflow of the component dependency analyzer is as follows: 1. When the function code and configuration are submitted, the static code analysis module 21 first performs code analysis. 2. Configuration integration module 22 then integrates the analysis results with the user configuration. 3. The dependency graph optimization module 23 generates an initial dependency graph based on the integration results. 4. The optimization module performs circular dependency detection and critical path analysis. 5. Finally, an optimized dependency graph is generated, including the loading priority and parallel loading strategy of each component.

[0063] To illustrate the effect of the component dependency analyzer, consider a serverless function that contains a machine learning model. Assume that the function contains three main components: data preprocessing, model loading, and inference. Through static analysis, the system may find that the data preprocessing component is frequently called, while model loading takes a long time. Combined with the model on-demand loading strategy specified in the user configuration, the resulting dependency graph will set the data preprocessing component to the highest priority, while allowing model loading to proceed in parallel with other lightweight components.

[0064] A key advantage of the component dependency analyzer is its high extensibility. For example, machine learning algorithms can be easily integrated to predict component usage patterns and further optimize the loading order. In addition, the analyzer is designed with standardized interfaces, allowing support for new programming languages ​​or frameworks to be easily added.

[0065] It should be noted that the performance of the component dependency analyzer has a significant impact on the startup time and resource consumption of the entire system. In actual deployment, it is recommended to perform performance tests on functions of different sizes and complexities, and adjust the depth and accuracy of the analysis as needed.

[0066] The component dependency analyzer described in this embodiment provides a strong foundation for asynchronous loading optimization of Serverless functions. By accurately analyzing the function structure and dependencies, combined with user configuration, an optimized loading strategy is generated, which significantly improves the startup performance and resource utilization efficiency of the function. This method not only solves the cold start problem faced by traditional Serverless platforms, but also makes it possible to develop more complex and efficient Serverless applications.

[0067] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art can adjust and expand the analyzer according to specific needs, such as integrating more advanced graph algorithms, or implementing optimization strategies for specific fields (such as deep learning). These changes and modifications should be considered to fall within the scope of protection of the present invention.

[0068] Embodiment 4: Priority loading scheduler.

[0069] This embodiment describes in detail the structure and working principle of the priority loading scheduler, which is one of the core innovative components of the system of the present invention. Figure 4 The priority loading scheduler mainly includes three modules: a priority queue management module 31 , a concurrency control module 32 and an error handling module 33 .

[0070] The main function of the priority queue management module 31 is to manage the priority queue of the components to be loaded. This module adopts an improved multi-level feedback queue algorithm, and its innovation lies in the ability to dynamically adjust the priority of the components. Specifically, the priority queue management module maintains queues of multiple priority levels, and a red-black tree structure is used inside each queue to ensure efficient insertion and deletion operations. The module also implements a dynamic priority adjustment mechanism, which adjusts the position of the component in the queue in real time according to factors such as the loading time, resource consumption, and call frequency of the component.

[0071] The priority calculation formula is as follows: P=α·I+β·T -1 +γ·F+δ·R -1 (1) Among them, P represents priority, I is the initial priority, T is the estimated loading time, F is the call frequency, R is the resource consumption, and α, β, γ, and δ are weight coefficients. This formula allows the system to flexibly balance the impact of different factors on the loading order.

[0072] The concurrency control module 32 is responsible for implementing a configurable concurrent loading mechanism. The core innovation of this module lies in its adaptive concurrency control algorithm. The system dynamically adjusts the concurrency based on the current available resources, network conditions, and component dependencies. The concurrency control module uses a token bucket algorithm to limit the number of components loaded concurrently, and introduces a machine learning-based prediction model to predict the loading time and resource consumption of each component, thereby optimizing the concurrency strategy.

[0073] The error handling module 33 is used to implement retry and downgrade strategies for components that fail to load. This module uses a multi-level retry mechanism combined with an exponential backoff algorithm to avoid the pressure on the system caused by frequent retries. The innovation lies in its downgrade strategy: when a component fails to load multiple times, the system automatically analyzes the dependency graph and tries to load an alternative version of the component or skips non-critical components to ensure that the core functions of the function can still run normally.

[0074] The overall workflow of the priority loading scheduler is as follows: 1. Receive the optimized dependency graph from the component dependency analyzer. 2. The priority queue management module 31 initializes each priority queue and allocates components to corresponding queues according to the initial priority. 3. The concurrency control module 32 determines the initial concurrency according to the current system status. 4. The scheduler starts to load components from the highest priority queue. 5. During the loading process, the priority queue management module 31 continuously adjusts the component priority. 6. The concurrency control module 32 adjusts the concurrency in real time to ensure optimal loading performance. 7. If a loading failure occurs, the error handling module 33 intervenes and executes a retry or downgrade strategy. 8. Repeat steps 4-7 until all components are loaded or the preset time limit is reached.

[0075] To illustrate the effect of a prioritized load scheduler, consider a Serverless function that includes user authentication, database connectivity, and a large machine learning model. The scheduler might first load the user authentication and database connectivity modules concurrently because they are lightweight and core functionality. Then, while waiting for these two modules to load, it starts loading the machine learning model. If the model fails to load, the system might try to load a smaller backup model to ensure that the function can still provide basic prediction capabilities.

[0076] A key advantage of the priority loading scheduler is its high configurability and adaptability. Developers can adjust the weight of the priority calculation formula through the configuration file, or define a custom loading strategy for a specific component. In addition, the system also reserves interfaces to allow the integration of more complex scheduling algorithms, such as genetic algorithms or reinforcement learning models, to further optimize the loading process.

[0077] It should be noted that the performance and effect of the priority load scheduler largely depends on accurate component information and system status. In actual deployment, it is recommended to combine the monitoring system to collect detailed loading time, resource usage and failure rate data for continuous optimization of scheduling strategies.

[0078] The priority loading scheduler described in this embodiment provides an efficient component loading mechanism for Serverless functions. Through dynamic priority adjustment, adaptive concurrency control and error handling, the startup performance and reliability of the function are significantly improved. This approach not only solves the cold start and resource utilization problems faced by traditional Serverless platforms, but also provides a foundation for building more complex and robust Serverless applications.

[0079] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art may adjust and expand the scheduler according to specific needs, such as integrating a new priority calculation model, or implementing an optimization strategy for a specific application scenario (such as a high-concurrency Web service). These changes and modifications should be considered to fall within the scope of protection of the present invention.

[0080] Embodiment 5: Partial function availability controller.

[0081] This embodiment describes in detail the structure and working principle of the partial function availability controller, which is one of the core innovative components of the system of the present invention. Figure 5 The partial function availability controller mainly includes three modules: a state tracking module 41, a request filtering module 42 and a degradation strategy execution module 43.

[0082] The main function of the state tracking module 41 is to maintain the loading status of each functional module. This module adopts an innovative hierarchical state management mechanism, which not only tracks the loading status of a single component, but also maintains the dependency status between components. Specifically, the state tracking module uses a directed acyclic graph (DAG) to represent component dependencies. Each node represents a component, and the attributes of the node include loading status, completion percentage, error information, etc. The state update adopts an efficient incremental update algorithm to ensure low latency even in large-scale component systems.

[0083] The status completion calculation formula is as follows: Among them, C represents the overall completion, n is the number of components, and w i is the weight of component i, s i is the loading progress of component i (a value between 0 and 1). This formula allows the system to give different weights to components of different importance, thereby more accurately reflecting the overall loading progress.

[0084] The request filtering module 42 is responsible for deciding which requests can be processed immediately based on the currently available functions. The core innovation of this module lies in its dynamic request routing algorithm. The system maintains a function dependency matrix that records the minimum set of functions required for each request type. When a request is received, the request filtering module quickly queries the currently available function set to determine whether the request can be processed. If it can be processed, the request is forwarded immediately; if it cannot be fully processed, the module evaluates whether a partial response can be provided or whether it needs to wait for more components to load.

[0085] The downgrade strategy execution module 43 is used to implement user-defined downgrade or waiting strategies for requests that rely on unloaded components. This module implements a configurable policy engine that supports multiple downgrade options. The innovation lies in its context-awareness: it dynamically selects the most appropriate downgrade strategy based on factors such as the importance of the request, the current system load, and the estimated waiting time. The strategy options include, but are not limited to: returning cached data, providing a simplified response, redirecting to an alternate service, or queuing the request until the full functionality is available.

[0086] The overall workflow of the partial function availability controller is as follows: 1. When the system starts, the state tracking module 41 initializes the component dependency graph. 2. When receiving component loading progress updates, the status tracking module 41 updates the corresponding node status and recalculates the overall completion degree. 3. When a request arrives, the request filtering module 42 quickly evaluates whether the currently available functions meet the request requirements. 4. If the function is satisfied, the request is processed immediately; otherwise, it goes to the downgrade strategy execution module 43. 5. The downgrade strategy execution module 43 analyzes the request context, selects and executes an appropriate downgrade strategy. 6. The status tracking module 41 continuously monitors the component loading status and notifies other modules when the loading of key components is completed. 7. The whole process repeats until all components are loaded or the system reaches a stable state.

[0087] To illustrate the effect of a partial availability controller, consider an e-commerce serverless function that includes user authentication, a product catalog, a shopping cart, and payment processing. When the system is first started, only user authentication and basic product catalog query functions may be available. At this point, users can browse products but cannot add to the shopping cart or check out. As more components are loaded, the system gradually enables advanced features. If the payment processing module is temporarily unavailable, the system may allow users to add items to the shopping cart, but will display a prompt that "checkout is temporarily unavailable."

[0088] A key advantage of the partial function availability controller is its flexibility and extensibility. Developers can define custom function dependencies and degradation strategies through configuration files. In addition, the system reserves a plug-in interface to allow the integration of domain-specific decision logic or machine learning models to further optimize request processing and degradation decisions.

[0089] It should be noted that the effectiveness of some functional availability control depends largely on reasonable component design and clear functional division. In practical applications, it is recommended to combine system monitoring and user feedback to continuously optimize component structure and degradation strategy to provide the best user experience.

[0090] The partial function availability controller described in this embodiment provides an innovative progressive startup mechanism for Serverless functions. Through precise state tracking, request filtering, and flexible degradation strategies, the availability and responsiveness of functions during cold start are significantly improved. This approach not only solves the all-or-nothing availability problem faced by traditional Serverless platforms, but also provides a foundation for building more resilient and user-friendly Serverless applications.

[0091] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art can adjust and expand the controller according to specific needs, such as implementing a more complex state tracking algorithm, or developing a customized degradation strategy for a specific application field (such as real-time data processing). These changes and modifications should be considered to fall within the scope of protection of the present invention.

[0092] Example 6: Resource monitoring and dynamic adjustment.

[0093] This embodiment describes in detail the structure and working principle of the resource monitoring and dynamic adjustment module, which is an important supplementary component of the system of the present invention. Figure 6 The resource monitoring and dynamic adjustment module mainly includes a resource monitoring submodule 51 , a resource prediction submodule 52 and a dynamic adjustment submodule 53 .

[0094] The main function of the resource monitoring submodule 51 is to monitor the resource usage of the component in real time. This submodule uses an innovative low-overhead sampling technology to collect detailed resource usage data without significantly affecting the function performance. Specifically, the resource monitoring submodule uses an adaptive sampling algorithm to dynamically adjust the sampling frequency according to the system load. High-frequency sampling is used to obtain accurate data when the load is low, and the sampling frequency is reduced to reduce overhead when the load is high. Monitoring indicators include but are not limited to CPU usage, memory usage, network I / O, disk I / O, etc.

[0095] The resource utilization efficiency calculation formula is as follows: Among them, E represents resource utilization efficiency, Tu is the effective usage time, Tt is the total time, R is the actual amount of resources used, and R is the amount of resources allocated. This formula allows the system to evaluate the resource utilization efficiency of each component and provide a basis for subsequent resource adjustments.

[0096] The resource prediction submodule 52 is responsible for predicting future resource requirements based on historical data. The core innovation of this submodule lies in its multi-model fusion prediction algorithm. The system runs multiple prediction models simultaneously, including time series analysis (such as ARIMA), machine learning models (such as LSTM neural networks), and heuristic rules. Then, through a dynamic weight allocation mechanism, the final prediction results are synthesized based on the historical accuracy of each model. This approach can adapt to different types of resource usage patterns and provide more accurate predictions.

[0097] The dynamic adjustment submodule 53 is used to dynamically adjust resource allocation according to monitoring data and prediction results. This submodule implements a closed-loop control system, and the innovation lies in the multi-objective optimization algorithm. The system considers multiple goals at the same time, including minimizing cold start time, maximizing resource utilization, ensuring service quality, etc. By solving the multi-objective optimization problem, the system can find the best balance between these potentially conflicting goals.

[0098] The resource adjustment decision function can be expressed as: D = argmin x (w1f1(x)+w2f2(x)+...+w n f n (x)) (4) Where D is the decision result, x is the adjustable resource parameter vector, and f i (x) is a different objective function, w i is the corresponding weight.

[0099] The overall workflow of the resource monitoring and dynamic adjustment module is as follows: 1. After the system is started, the resource monitoring submodule 51 starts to periodically collect resource usage data. 2. The resource forecasting submodule 52 forecasts short-term and medium-term resource requirements based on historical data and current trends. 3. The dynamic adjustment submodule 53 comprehensively considers the monitoring data, prediction results and system goals to calculate the optimal resource allocation. 4. The system performs resource adjustments, which may include expanding or shrinking computing resources, adjusting memory allocation, changing network configuration, etc. 5. After adjustment, the resource monitoring submodule 51 continues to collect data to form a closed-loop feedback. 6. The whole process continues to ensure that the system resource configuration is always in the optimal state.

[0100] To illustrate the effect of the resource monitoring and dynamic adjustment module, consider a Serverless function that processes an image recognition task. During low load periods, the system may reduce the number of allocated CPU cores and memory to save resources. When an increase in request volume is detected, the system will increase resource allocation in advance, such as increasing the number of CPU cores from 2 to 4 and memory from 2GB to 4GB a few minutes in advance, thereby avoiding performance bottlenecks during peak periods.

[0101] A key advantage of the resource monitoring and dynamic adjustment module is its adaptability and scalability. The module can be seamlessly integrated into various Serverless platforms, and monitoring indicators and adjustment strategies can be customized according to specific application scenarios. In addition, the system reserves an API interface to allow the integration of third-party monitoring tools or customized resource management strategies.

[0102] It should be noted that dynamic adjustment of resources may temporarily affect the performance of the function. Therefore, in practical applications, it is recommended to set appropriate adjustment thresholds and cooling periods to avoid frequent resource fluctuations. At the same time, the cost of resource adjustment should also be considered to find a balance between performance improvement and resource costs.

[0103] The resource monitoring and dynamic adjustment module described in this embodiment provides a resource management mechanism for Serverless functions. Through precise resource monitoring, predictive demand analysis, and multi-objective optimization adjustment strategies, the resource utilization efficiency and performance stability of the function are significantly improved. This approach not only solves the resource waste and performance fluctuation problems faced by traditional Serverless platforms, but also provides a foundation for building more economical, efficient, and reliable Serverless applications.

[0104] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art can adjust and expand the module according to specific needs, such as integrating more complex prediction algorithms, or developing optimization strategies for specific cloud platforms. These changes and modifications should be considered to fall within the scope of protection of the present invention.

[0105] Example 7: Cross-platform adaptation and performance optimization.

[0106] This embodiment describes in detail the cross-platform adaptation and performance optimization mechanism of the system of the present invention. This is a comprehensive embodiment that shows how to integrate and optimize the various components in the previous embodiments to adapt to different Serverless platforms and application scenarios. Figure 7 As shown, the cross-platform adaptation and performance optimization mechanism mainly includes a platform abstraction layer 61, an adapter 62 and a performance optimization engine 63.

[0107] The main function of the platform abstraction layer 61 is to provide a unified interface to shield the differences between different Serverless platforms. This layer uses innovative dynamic proxy technology to automatically generate adaptation code at runtime based on the characteristics of the target platform. Specifically, the platform abstraction layer implements a set of common APIs that cover key operations such as function initialization, resource allocation, and state management. When the system is deployed to a specific platform, the abstraction layer dynamically generates a conversion layer to map common API calls to platform-specific APIs.

[0108] The API mapping function can be expressed as: F target =M(F generic ,P target ) (5) Among them, F target is the API function of the target platform, F generic is a general API function, M is a mapping function, P target A set of characteristic parameters for the target platform.

[0109] The adapter 62 is responsible for dynamically adjusting the behavior and configuration of the system based on the characteristics and limitations of the target platform. The core innovation of this adapter is its self-learning ability. When the system runs on different platforms, it continuously collects performance data and platform limitation information, and uses reinforcement learning algorithms to continuously optimize the adaptation strategy. For example, on a resource-constrained platform, the adapter may automatically reduce concurrency or adjust the granularity of component loading to adapt to the platform's limitations.

[0110] The adaptation strategy optimization can be expressed as a Markov decision process (MDP):

[0111] Among them, V * (s) is the optimal value function of state s, a is the action, R(s,a) is the immediate reward, γ is the discount factor, P(s ′ |s,a) is the state transition probability.

[0112] The performance optimization engine 63 is used to globally optimize system performance. The engine implements an innovative multi-level optimization framework, including compile-time optimization, load-time optimization, and runtime optimization. At compile time, the engine analyzes the function code and performs optimizations such as dead code elimination and inline expansion. At load time, the engine dynamically decides whether to reorder the code or delay loading based on platform characteristics and current resource conditions. At runtime, the engine continuously monitors function execution, identifies hot code, and performs just-in-time compilation optimization.

[0113] The performance optimization objective function can be expressed as: O=w1T s +w2T e +w3M+w4C (7) Among them, O is the overall optimization goal, T s is the start-up time, T e is the execution time, M is the memory usage, C is the computing resource usage, and w i is the corresponding weight.

[0114] The overall workflow of the cross-platform adaptation and performance optimization mechanism is as follows: 1. When the system is initialized, the platform abstraction layer 61 identifies the current operating platform and generates a corresponding API mapping. 2. The adapter 62 loads the historical learning data and initializes the adaptation strategy. 3. The performance optimization engine 63 performs static analysis and preliminary optimization on the function code. 4. When a function is called, the adapter 62 selects the optimal loading and execution strategy based on the current platform status. 5. The performance optimization engine 63 continuously performs dynamic optimization during function execution. 6. After execution is completed, the system collects performance data and updates the adapter’s learning model and optimization strategy.

[0115] To illustrate the effect of cross-platform adaptation and performance optimization mechanisms, consider a machine learning inference function that needs to be migrated between AWS Lambda and Google Cloud Functions. On AWS Lambda, the system may choose a higher concurrency and a more aggressive preloading strategy because Lambda allows longer execution time and larger memory allocation. On Google Cloud Functions, the system may adopt a more conservative resource usage strategy, such as increasing the granularity of code sharding and reducing the number of components loaded at one time. Through this adaptation, the same function can achieve near-optimal performance on different platforms.

[0116] A key advantage of the cross-platform adaptation and performance optimization mechanism is its high scalability and adaptability. This mechanism can not only adapt to existing mainstream Serverless platforms, but also quickly support emerging platforms and technologies. In addition, through continuous learning and optimization, the system can continuously improve its performance in various environments over time.

[0117] It should be noted that cross-platform adaptation and performance optimization may introduce certain complexity and overhead. In actual applications, it is recommended to carefully select the optimization features to enable based on the specific application requirements and the characteristics of the target platform to achieve the best balance between flexibility and performance.

[0118] The cross-platform adaptation and performance optimization mechanism described in this embodiment provides powerful portability and performance guarantee for Serverless functions. Through platform adaptation, self-learning optimization strategy and all-round performance optimization, the adaptability and execution efficiency of functions in different environments are significantly improved. This method not only solves the challenges of cross-platform deployment of Serverless applications, but also provides an innovative solution for building high-performance and highly portable Serverless systems.

[0119] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art can adjust and expand the mechanism according to specific needs, such as integrating a new platform feature recognition algorithm, or developing a customized optimization strategy for a specific application field. These changes and modifications should be considered to fall within the scope of protection of the present invention.

[0120] Example 8: Cache and state management.

[0121] This embodiment describes in detail the cache and state management mechanism of the system of the present invention. This is a comprehensive embodiment that shows how to further optimize the performance and reliability of Serverless functions through efficient cache strategies and state management. Figure 8As shown, the cache and state management mechanism mainly includes a multi-level cache system 71, a state persistence engine 72 and a preheating module 73.

[0122] The main function of the multi-level cache system is to optimize the efficiency of data access during function execution. The system uses an innovative hierarchical cache architecture, including memory cache, local persistent storage, and distributed cache. The core innovation of the system lies in its adaptive cache strategy, which can dynamically adjust the cache level and invalidation strategy according to the access pattern, size, and importance of the data. Specifically, the multi-level cache system uses machine learning algorithms to predict the future access probability of data, and based on this, determines the cache location and retention time of the data.

[0123] The cache utility function can be expressed as: U(d)=P(d)·V(d)-C(d) (8) Among them, U(d) is the cache utility of data item d, P(d) is the access probability, V(d) is the data value, and C(d) is the cache cost. The system will give priority to caching data items with high utility.

[0124] The state persistence engine 72 is responsible for managing the state information during the function execution process, ensuring that the state can be correctly restored after the function instance is terminated. The core innovation of this engine lies in its incremental state synchronization mechanism. Traditional methods usually save the entire state at once when the function execution ends, while this system adopts a real-time incremental synchronization strategy. Whenever a key change occurs in the state, the system will immediately synchronize the changed part to the persistent storage, greatly reducing the risk of state loss and the amount of data transmission during recovery.

[0125] The incremental synchronization strategy can be expressed in the following pseudo code:

[0126] The preheating module 73 is used to predictively load and cache data and states that may be needed. This module implements an innovative predictive loading framework that combines historical access pattern analysis and context-aware technology to accurately predict future data needs. For example, for a function that processes user requests, the system may preload the user's preferences and recently accessed data after detecting a user login event.

[0127] The preheating priority calculation formula is as follows: P(i)=w1F(i)+w2R(i)+w3C(i) (9) Among them, P(i) is the preheating priority of item i, F(i) is the historical access frequency, R(i) is the time of the most recent access, C(i) is the current context relevance, and w i is the corresponding weight.

[0128] The overall workflow of the cache and state management mechanism is as follows: 1. When the function is initialized, the preheating module 73 determines the data and status that need to be preloaded based on historical data and current context. 2. The multi-level cache system 71 loads data into the appropriate cache level according to the preheating instruction. 3. During function execution, the multi-level cache system 71 continuously optimizes the distribution of data between different cache layers. 4. The state persistence engine 72 monitors state changes in real time and performs incremental synchronization. 5. After the function is executed, the system evaluates the effectiveness of the cache and warm-up strategies and updates the prediction model.

[0129] To illustrate the effect of the cache and state management mechanism, consider a Serverless function that processes e-commerce orders. During peak processing, the system may predict that a large number of order query requests will be coming, so the inventory information and price data of popular products are loaded into the memory cache in advance. At the same time, changes in order status are saved in real time through incremental synchronization to ensure that even if the function instance is terminated unexpectedly, it can be quickly restored to the latest state. This approach significantly reduces data access latency and improves system throughput and reliability.

[0130] A key advantage of the cache and state management mechanism is its high adaptability. The mechanism can automatically adjust the cache and synchronization strategy as the application mode changes without manual intervention. In addition, through fine-grained incremental synchronization and predictive loading, the system greatly reduces data transmission and storage costs.

[0131] It should be noted that advanced caching and state management may increase the complexity of the system. In practical applications, it is recommended to carefully configure the granularity of preheating and synchronization according to the characteristics and performance requirements of the function to strike a balance between performance improvement and system complexity.

[0132] The cache and state management mechanism described in this embodiment provides powerful performance optimization and reliability guarantee for Serverless functions. Through multi-level caching, incremental state synchronization and preheating, the data access efficiency and state recovery capability of the function are significantly improved. This method not only solves the state management and cold start problems faced by traditional Serverless platforms, but also provides an innovative solution for building high-performance and high-reliability Serverless applications.

[0133] Finally, although this embodiment describes a specific implementation in detail, the scope of the present invention is not limited thereto. Those skilled in the art can adjust and expand the mechanism according to specific needs, such as integrating more complex data access pattern analysis algorithms, or developing customized caching strategies for specific types of data. These changes and modifications should be considered to fall within the scope of protection of the present invention.

Claims

1. A system for optimizing Serverless function loading, characterized in that: include: (a) A user-defined asynchronous loading configurator, used to receive and process the asynchronous loading strategy of modules or functions specified by the developer; (b) a component dependency analyzer, used to generate a component dependency graph based on the asynchronous loading strategy and the function code structure; (c) a priority-based loading scheduler for scheduling the loading order of components according to the component dependency graph and user-defined priorities; (d) Partial function availability controller, which is used to manage the partially available state of a function, enabling the function to respond to specific requests before it is fully loaded.

2. The system according to claim 1, characterized in that The user-defined asynchronous loading configurator includes: (a) A configuration file parsing module, used to parse the standard format configuration file provided by the developer; (b) a code annotation recognition module, used to recognize asynchronous loading annotations in function codes; (c) Configuration verification module, used to verify the validity and consistency of the user-defined configuration.

3. The system according to claim 1, characterized in that The component dependency analyzer includes: (a) A static code analysis module, which is used to analyze the abstract syntax tree of the function source code and identify module imports and function call relationships; (b) a configuration integration module, used to combine the user-defined asynchronous loading configuration with the static analysis results; (c) Dependency graph optimization module, used to generate optimized loading order based on the analysis results.

4. The system according to claim 1, characterized in that The priority-based loading scheduler includes: (a) a priority queue management module, used to manage the priority queue of components to be loaded; (b) a concurrency control module, used to implement a configurable concurrent loading mechanism; (c) Error handling module, used to implement retry and downgrade strategies for components that fail to load.

5. The system according to claim 1, characterized in that Some of the functional availability controllers include: (a) A status tracking module, used to maintain the loading status of each functional module; (b) a request filtering module, which is used to decide which requests can be processed immediately based on the currently available functions; (c) A downgrade strategy execution module, which is used to implement a user-defined downgrade or wait strategy for requests that depend on unloaded components.

6. The system according to claim 1, characterized in that It also includes a resource monitoring module, which is used to monitor the resource usage of each component in real time and dynamically adjust resource allocation based on the monitoring results.

7. The system according to claim 1, characterized in that The user-defined asynchronous loading configurator supports defining asynchronous loading strategies through configuration files in YAML or JSON format.

8. A method for optimizing Serverless function loading, characterized in that: The following steps are involved: (a) Receive and process the asynchronous loading strategy of modules or functions specified by the developer; (b) generating a component dependency graph based on the asynchronous loading strategy and the function code structure; (c) scheduling the loading order of components according to the component dependency graph and user-defined priorities; (d) Manage the partially available state of a function so that the function can respond to specific requests before it is fully loaded.

9. The method according to claim 8, characterized in that The following steps are also included: (a) Identify module imports and function call relationships through static code analysis; (b) Combining user-defined asynchronous loading configuration with static analysis results; (c) Generate an optimized loading sequence based on the analysis results.

10. The method according to claim 8, characterized in that The following steps are also included: (a) Monitor the resource usage of each component in real time; (b) Dynamically adjust resource allocation based on monitoring results; (c) Implement retry and downgrade strategies for components that fail to load.

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