Python Serverless function cold start optimization system and method

Abstract

The invention provides a cold start optimization system and a cold start optimization method for a Python Serverless function. The system comprises a dependency analysis module, a module pre-compiling system, a hierarchical module loader, an inert import mechanism, a code cache optimizer and a self-adaptive optimization system. The dependency analysis module analyzes code dependency in a function deployment stage; the module pre-compiling system pre-compiles the source code into an optimized byte code; the hierarchical module loader loads modules of different hierarchies according to priorities; actual loading of an inert import mechanism delay module; the code cache optimizer caches a hotspot function and a module state; and the self-adaptive optimization system dynamically adjusts preloading and caching strategies. The system further comprises a JIT compiling intensifier, a state storage and quick recovery module, a memory management optimizer and a security mechanism. The method has the beneficial effects that the function cold start time is remarkably shortened, the function execution efficiency is improved, the resource utilization is optimized, and meanwhile, the safety and the expandability are ensured.

Description

Technical Field

[0001] The present invention relates to the fields of cloud computing and software optimization, and in particular to a system and method for optimizing the performance of the Python programming language in a Serverless computing environment. Background Art

[0002] As a widely used high-level programming language, Python has important applications in cloud computing and serverless architecture. The serverless computing model has been rapidly developed and widely used in recent years due to its advantages such as on-demand use, automatic expansion, and reduced operation and maintenance costs. However, when running Python functions in a serverless environment, the long cold start time has always been a key issue that restricts its performance and user experience.

[0003] Currently, solutions such as preheating function instances and optimizing dependency loading are mainly used to reduce the cold start time of Python Serverless functions. The preheating function instance technology keeps the instance active by triggering the function regularly, thereby avoiding cold starts. The optimized dependency loading solution reduces loading time by streamlining dependency libraries, using lightweight alternative libraries, or packaging commonly used libraries into function deployment packages. In addition, some cloud service providers also provide specialized Python runtime optimizations, such as AWS Lambda's Custom Runtime.

[0004] However, these solutions have some problems. Although preheating function instances can effectively reduce cold start time, it will increase costs and may violate the on-demand use principle of Serverless. Although optimizing dependency loading can improve startup speed to a certain extent, it often requires developers to optimize manually and may affect the functional integrity of the function. Although specialized runtime optimization is effective, it is usually bound to a specific cloud platform, limiting the portability of the application.

[0005] To solve the above problems, some studies have proposed more intelligent solutions, such as dynamic dependency analysis and on-demand loading technology. These methods use static analysis and runtime tracing to identify the dependencies that are really needed, and dynamically load the required modules during function execution. Although this method improves the cold start time and resource utilization efficiency to a certain extent, it still has problems such as insufficient analysis accuracy and high dynamic loading overhead, making it difficult to meet the demand for extremely fast startup.

[0006] Therefore, a more comprehensive and efficient cold start optimization solution for Python Serverless functions is urgently needed. This solution should comprehensively consider multiple aspects such as dependency analysis, code precompilation, intelligent loading, and runtime optimization to significantly reduce the cold start time while maintaining the functional integrity and cross-platform compatibility of the function. Developing such a systematic optimization solution has become a research hotspot and urgent need in the field of Python Serverless computing. Summary of the invention

[0007] The main purpose of the present invention is to solve the problem of long cold start time of Python Serverless functions, especially for functions with complex dependencies and large code volume, and significantly reduce their initialization time. In addition, the present invention also aims to improve the overall execution efficiency of Python Serverless functions, optimize resource utilization, while maintaining cross-platform compatibility and system security.

[0008] To achieve the above object, the present invention provides a Python Serverless function cold start optimization system and method, characterized in that the system includes a dependency analysis module, a module precompilation system, a hierarchical module loader, a lazy import mechanism, a code cache optimizer, and an adaptive optimization system. In some embodiments, the system can also integrate a JIT compilation enhancer, a state preservation and fast recovery module, a memory management optimizer, and a security mechanism to further improve performance and functionality.

[0009] Specifically, the dependency analysis module is used to analyze the function code and its dependencies during the function deployment phase and generate a dependency graph. This module not only performs static analysis, but also includes a dynamic tracking unit to record the actual imported modules and usage order at runtime, thereby providing more accurate dependency information.

[0010] Furthermore, the module precompilation system precompiles Python source code into optimized bytecode or machine code. The system supports incremental compilation, compiling only the changed parts, and can generate optimized code specific to the target serverless environment to maximize performance improvement.

[0011] Preferably, the hierarchical module loader loads modules of different levels according to predefined priorities. The loader divides the modules into core layer, function layer, library layer and dynamic layer, and loads them according to the hierarchical priority to ensure that the key modules are initialized first.

[0012] Optionally, a lazy import mechanism is used to delay the actual loading and initialization of the module. The mechanism includes a custom import hook that creates a placeholder object when an import statement is encountered, triggering real loading and initialization only when the module is actually used, thereby reducing unnecessary loading overhead.

[0013] In one embodiment, the code cache optimizer is used to cache the status of hot functions, commonly used objects, and loaded modules. This includes not only function-level cache, but also object cache and module cache, effectively reducing the time of repeated initialization.

[0014] In some embodiments, the system also includes a JIT compilation enhancer for integrating or improving the existing Python JIT compiler, optimizing the JIT strategy for the serverless environment, and supporting the saving of JIT compilation results across calls to further improve execution efficiency.

[0015] In addition, the present invention may also include a state saving and fast recovery module for saving the key state of the Python interpreter after the function is executed, and quickly rebuilding the interpreter state in a new call, significantly reducing the cold start time.

[0016] In other embodiments, the system integrates a memory management optimizer to implement a special memory allocation strategy for short-lifecycle functions, and optimizes the garbage collection algorithm to reduce GC pause time and improve function response speed.

[0017] In a preferred embodiment, the adaptive optimization system continuously monitors module loading, code execution, and memory usage, and dynamically adjusts preloading and caching strategies based on this data. This adaptive mechanism can always maintain optimal performance under different function call patterns and load conditions.

[0018] In another preferred embodiment, the system includes a security mechanism and an API integration module. The security mechanism implements module-level isolation and version control to ensure that different function instances do not affect each other. The API integration module provides an interface that allows developers to specify preloaded or lazy loaded modules, and designs an integration solution with the mainstream Python serverless platform, which improves the flexibility and availability of the system.

[0019] By adopting the above scheme, the present invention has the following beneficial effects: (1) Significantly reduce the cold start time of Python Serverless functions, improving function response speed and user experience; (2) Through intelligent dependency analysis and hierarchical loading, the module loading process is optimized and resource utilization efficiency is improved; (3) The execution efficiency of Python code is improved by using precompilation and JIT compilation technology; (4) The adaptive optimization system can dynamically adjust strategies according to actual operating conditions to ensure optimal performance in different scenarios; (5) By providing security mechanisms and API integration, the security and scalability of the system are enhanced, making it easier to deploy and use on different serverless platforms.

[0020] In summary, the present invention provides a comprehensive and efficient Python Serverless function optimization solution, which not only solves the problem of long cold start time, but also brings significant improvements in overall performance, resource utilization, and system flexibility. It is of great significance to promote the application of Python in the field of serverless computing. 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 overall architecture diagram of the Python Serverless function cold start optimization system. It shows the six main modules of the system (dependency analysis module 101, module precompilation system 102, hierarchical module loader 103, lazy import mechanism 104, code cache optimizer 105 and adaptive optimization system 106) and the interactions between them.

[0023] Figure 2 It is a flowchart of dependency analysis and module precompilation. It describes the entire process from source code to optimized compilation results, including static analysis, dynamic tracing, dependency graph generation, module precompilation, and incremental compilation.

[0024] Figure 3 It is a flowchart of hierarchical module loading and lazy import. It shows the hierarchical structure of module loading (core layer, function layer, library layer, dynamic layer) when a function is called, as well as the workflow of the lazy import mechanism.

[0025] Figure 4 It is a flowchart of code caching and adaptive optimization. It describes the multi-level caching strategy of the code cache optimizer and the workflow of the adaptive optimization system, including monitoring indicator collection, decision engine, and optimization strategy adjustment.

[0026] Figure 5 This is a flowchart of state preservation and fast recovery. It shows the state preservation process after the function is executed and the fast recovery process when it is called again, including steps such as state snapshot generation, incremental storage, and fast reconstruction.

[0027] Figure 6It is a JIT compilation and memory management optimization flow chart. It describes the workflow of the JIT compilation enhancer (including lightweight bytecode optimization and trace-based JIT compilation) and the main functions of the memory management optimizer.

[0028] Figure 7 It is a security mechanism and API integration flow chart. It shows the system's security mechanism (including module-level isolation, code integrity verification, sensitive data encryption, and version control) and the main components and functions of the API integration module. DETAILED DESCRIPTION

[0029] 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.

[0030] 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 should not be understood as a limitation on the present invention.

[0031] Example 1: Overall system architecture.

[0032] Reference Figure 1 The overall architecture of the Python Serverless function cold start optimization system provided by the present invention includes the following main modules: dependency analysis module 101, module precompilation system 102, hierarchical module loader 103, lazy import mechanism 104, code cache optimizer 105 and adaptive optimization system 106. These modules work together to form a complete optimization chain, which significantly reduces the cold start time of Python Serverless functions.

[0033] The dependency analysis module 101 is the foundation of the system. It accurately identifies the dependencies of functions through an innovative method that combines static analysis and dynamic tracing. Static analysis uses the abstract syntax tree (AST) technology to deeply analyze the function code structure and identify import statements and function calls. Dynamic tracing uses a custom import hook to record the order and frequency of module loading when the function is actually running. This dual analysis method can not only identify direct dependencies, but also discover indirect dependencies and conditional imports, greatly improving the accuracy and completeness of the dependency graph.

[0034] The module precompilation system 102 uses the results of dependency analysis to precompile the identified modules. The innovation here is that precompilation not only generates ordinary bytecodes, but also generates optimized machine codes based on the characteristics of the target Serverless environment (such as CPU architecture, memory limitations, etc.). The precompilation system uses an improved incremental compilation algorithm and only recompiles the changed code parts, which greatly reduces compilation time and resource consumption. The results of precompilation are stored in a specific cache area for fast loading.

[0035] The hierarchical module loader 103 is one of the core innovations of this system. It divides modules into four layers: core layer, function layer, library layer, and dynamic layer. The core layer contains the basic modules required for Python runtime; the function layer contains function definitions and direct dependencies; the library layer contains commonly used third-party libraries; and the dynamic layer is the module that is dynamically loaded according to actual calls. The loader uses a priority queue algorithm to ensure that important modules are loaded first. This hierarchical loading strategy significantly reduces the initial loading time while ensuring the normal operation of the function.

[0036] The lazy import mechanism 104 delays the actual loading time of the module through the innovative placeholder technology. When encountering an import statement, the system does not load the module immediately, but creates a lightweight placeholder object. This placeholder object contains the module's metadata, but does not perform actual module initialization. Only when the code actually uses the objects in the module will the actual loading and initialization process be triggered. This mechanism greatly reduces unnecessary module loading and significantly shortens the startup time of the function.

[0037] Code Cache Optimizer 105 uses an innovative multi-level cache strategy. It not only caches compiled bytecodes, but also caches the machine code of hot functions, serialized versions of commonly used objects, and the initialization status of modules. The cache strategy uses a combination of the LRU (least recently used) algorithm and access frequency analysis to ensure that the most important and most frequently used data is always in the cache. This multi-level cache strategy significantly reduces the time for repeated compilation and initialization.

[0038] The adaptive optimization system 106 is another innovative feature of the present invention. It continuously monitors the execution of functions, including module loading time, function execution time, memory usage, etc. Based on this data, the system uses machine learning algorithms (such as reinforcement learning) to dynamically adjust the preloading strategy, cache strategy, and memory allocation strategy. For example, if a module is found to be frequently used, the system will move it to a higher loading priority; if a function is frequently called, the system will optimize its cache strategy.

[0039] The data flow between these modules is as follows Figure 1As shown. The output of dependency analysis module 101 is used by module precompilation system 102 and hierarchical module loader 103. The output of precompilation system 102 is managed by code cache optimizer 105. Lazy import mechanism 104 works closely with hierarchical module loader 103 to control the actual loading time of the module. Adaptive optimization system 106 receives the operation data of all other modules and provides them with optimization suggestions.

[0040] In the Serverless environment, this system is deployed as an intermediate layer between the function execution environment and the underlying Python interpreter. When a function is called, the system first takes over control, executes the optimization process, and then hands control over to the actual function code. This deployment method enables the system to be seamlessly integrated into the existing Serverless platform without modifying the platform's own code.

[0041] The workflow of this system is as follows: (1) When a function is deployed, the dependency analysis module 101 analyzes the function code and generates an initial dependency graph; (2) The module pre-compilation system 102 pre-compiles related modules according to the dependency graph; (3) When a function is called, the hierarchical module loader 103 loads necessary modules according to priority; (4) The lazy import mechanism 104 creates placeholders for modules that are not loaded immediately; (5) During function execution, the code cache optimizer 105 provides cached code and objects; (6) The adaptive optimization system 106 monitors the execution status throughout the process and adjusts the optimization strategy in real time.

[0042] Through this innovative system architecture and workflow, the present invention can significantly reduce the cold start time of Python Serverless functions. It should be noted that the above embodiment is only one of the preferred implementations of the present invention. Those skilled in the art can adjust the system architecture according to actual needs, such as adding additional optimization modules or adjusting the interaction mode between modules. In addition, although this embodiment is mainly aimed at the Python language, the same principles and methods can also be applied to the optimization of Serverless functions in other interpreted languages. The scope of protection of the present invention is not limited to the above embodiments, but should include various equivalent changes and modifications.

[0043] Embodiment 2: Dependency analysis and module precompilation.

[0044] This embodiment describes in detail the implementation details of the dependency analysis module and the module precompilation system. These two modules are key components of the system of the present invention for optimizing the cold start time of Python Serverless functions.

[0045] Dependency analysis module (refer to Figure 1 101) adopts an innovative dual analysis strategy that combines static analysis and dynamic tracing to obtain more precise and comprehensive dependency information.

[0046] The static analysis part uses an improved abstract syntax tree (AST) technology. The system first parses the Python source code into AST, then traverses the AST nodes to identify all import statements and function calls. Unlike traditional static analysis, this system also analyzes conditional imports and dynamic imports (such as __import__ function calls). In order to handle these complex situations, the system implements a path analysis algorithm based on symbolic execution. The algorithm simulates different execution paths of the code and identifies the modules that may be imported in each possible execution branch.

[0047] The dynamic tracking part uses Python's sys.meta_path mechanism to implement a custom import hook. This hook intercepts all import operations at runtime and records the loading order, loading frequency and loading time of modules. In particular, this system also records the initialization time and memory usage of each module, which is crucial for subsequent optimization decisions.

[0048] The dependency analysis module also implements an innovative dependency weight calculation algorithm. This algorithm takes into account factors such as import frequency obtained from static analysis, loading frequency recorded by dynamic tracking, module initialization time and memory usage, and assigns a weight value to each dependency. This weight value will be used to guide the decision of module precompilation and loading priority.

[0049] Module precompilation system (see Figure 1 102) Based on the results of dependency analysis, the identified modules are precompiled. The innovation of this system is that it not only generates standard Python bytecode, but also generates optimized machine code according to the characteristics of the target Serverless environment.

[0050] The precompilation process first generates bytecode using Python's built-in compile function. Then, the system uses a customized just-in-time compiler (JIT compiler) to further compile the bytecode of the hot function into machine code. This JIT compiler is specially optimized to take into account the characteristics of the Serverless environment, such as short execution time and limited memory. For example, it uses a more aggressive inlining strategy and optimizes the memory allocation pattern to adapt to the execution of short-lived functions.

[0051] Another innovation of the precompilation system is the incremental compilation strategy. The system maintains a persistent compilation cache that records the compilation results and source code hash values ​​of each module. When the function code is updated, the system only recompiles the changed modules, greatly reducing the compilation time. To achieve this, the system uses an efficient file difference comparison algorithm to quickly identify the changed parts of the code.

[0052] In addition, the precompilation system also implements a code optimization pipeline, including the following innovative optimization steps: Dead Code Elimination: Identify and remove code paths that are never executed.

[0053] Constant folding: Evaluate constant expressions at compile time.

[0054] Loop optimization: including loop unrolling and vectorization to improve execution efficiency.

[0055] Specialized optimization: Based on the characteristics of the Serverless environment, some general library functions are specialized.

[0056] The precompiled result is stored in an optimized binary format designed for fast loading and execution. It contains not only the compiled code, but also the module's metadata and initialization state for fast restoration when a function is called.

[0057] The dependency analysis module and the module precompilation system work closely together to form a feedback optimization loop. The precompilation system adjusts the compilation strategy based on the results of the dependency analysis, and the dependency analysis module adjusts the dependency weights based on the actual compilation and execution results.

[0058] This innovative dependency analysis and precompilation method significantly reduces the cold start time of Python Serverless functions. It not only reduces the number of modules that need to be loaded at runtime, but also improves the execution efficiency of the code through precompilation and optimization. At the same time, the incremental compilation strategy also greatly reduces the delay when the function is updated.

[0059] Example 3: Hierarchical module loading and lazy import.

[0060] This example describes the implementation details of the hierarchical module loader and lazy import mechanism. These two modules work together to further optimize the startup process and runtime performance of Python Serverless functions.

[0061] Hierarchical module loader (see Figure 1103) adopts an innovative multi-level loading strategy, dividing modules into four levels: core layer, function layer, library layer and dynamic layer. The core idea of ​​this layered strategy is to determine the loading order and method based on the importance and frequency of use of modules, so as to maximize the startup performance.

[0062] The core layer contains the basic modules required for Python runtime, such as sys, os, etc. These modules are preloaded into memory when the function instance is created to ensure the rapid preparation of the basic operating environment. The function layer contains the function definition itself and the directly dependent modules, which are loaded immediately when the function is first called. The library layer contains commonly used third-party libraries, such as numpy, pandas, etc. These modules adopt a lazy loading strategy and are only loaded when they are actually used. The dynamic layer contains modules that are dynamically imported according to runtime conditions. These modules rely entirely on the lazy import mechanism.

[0063] The hierarchical module loader uses an innovative priority queue algorithm to manage the loading order. The priority calculation takes into account factors such as layer weight, historical usage frequency, dependency depth, and average loading time. This algorithm ensures that the most important and most frequently used modules are loaded first, greatly improving the startup speed of functions.

[0064] To further optimize the loading process, the hierarchical module loader also implements innovative features such as parallel loading, memory mapping, and module preheating. Parallel loading uses multi-threading technology to load independent modules at the same time, making full use of multi-core CPUs. Memory mapping technology is used to load large modules, reducing memory usage and increasing loading speed. The module preheating mechanism pre-executes the initialization operation of some high-frequency modules when the function instance is created based on historical call data.

[0065] Lazy import mechanism (see Figure 1 104) is another key innovation of this system. It significantly reduces the cold start time of the function by delaying the actual loading and initialization time of the module. The core of the lazy import mechanism is a custom import hook that intercepts all import statements and implements a series of innovative features.

[0066] When an import statement is encountered, the system does not load the module immediately, but creates a lightweight placeholder object. This placeholder object contains the module's metadata, such as the module name, file path, etc., but does not execute the actual module code. Only when the code actually accesses the attributes or functions in the module will the system trigger the real module loading and initialization process. This is achieved by overriding the __getattr__ method of the placeholder object.

[0067] For large modules, the system implements a partial loading mechanism. Only submodules and functions that are actually used will be loaded instead of loading the entire module. This fine-grained loading control greatly reduces memory usage and loading time. The system also maintains an accurate module loading status tracker that records the loading status of each module (not loaded, partially loaded, fully loaded). This helps the adaptive optimization system make smarter decisions.

[0068] The lazy import mechanism also includes an innovative circular dependency resolution algorithm that can elegantly handle circular dependency problems between modules and avoid deadlocks. This solves a long-standing problem in the Python module system and improves the stability and reliability of the system.

[0069] The layered module loader and lazy import mechanism work closely together. When the layered module loader decides that a module should be lazy loaded, it notifies the lazy import mechanism to create a corresponding placeholder object. At the same time, the lazy import mechanism also reports the actual module loading status to the layered module loader to help it adjust the loading strategy.

[0070] This innovative layered loading and lazy import method brings significant performance improvements. It not only greatly reduces the cold start time of the function, but also optimizes memory usage and improves resource utilization efficiency. This method is especially effective for Serverless functions with complex dependencies and large amounts of code.

[0071] Embodiment 4: Code caching and adaptive optimization.

[0072] This embodiment describes in detail the implementation details of the code cache optimizer and the adaptive optimization system. The two modules work together to further improve the execution efficiency and resource utilization of Python Serverless functions.

[0073] Code Cache Optimizer (see Figure 1 105) in the , adopts an innovative multi-level cache strategy, which not only caches the compiled bytecode, but also caches the machine code of hot functions, the serialized version of commonly used objects, and the initialization status of modules. This multi-level cache strategy significantly reduces the time of repeated compilation and initialization, and greatly improves the execution speed of functions.

[0074] The core of the cache system is an efficient key-value storage structure that uses an improved LRU (least recently used) algorithm to manage cache items. Unlike the traditional LRU algorithm, this system introduces a dynamic weight factor that takes into account factors such as access frequency, recent access time, item size, and loading time. The weight calculation formula for cache items is as follows: W = (F * R) / (S * T) Among them, F is the access frequency, R is the inverse of the time of the most recent access, S is the item size, and T is the loading time. This formula ensures that the most valuable cache items can remain in the cache for a long time.

[0075] In order to adapt to the characteristics of the Serverless environment, the cache system implements innovative features such as persistent caching, layered caching, incremental updates, and compressed storage.

[0076] Persistent Cache: Use the persistent storage provided by the Serverless platform to save some cache content outside the function instance and quickly restore it during cold start. This greatly reduces the initialization time during cold start.

[0077] Tiered caching: The cache is divided into a memory layer and a persistence layer. Hot data is stored in memory to provide the fastest access speed, while other data is stored in the persistence layer.

[0078] Incremental updates: When function code is updated, the cache system can intelligently identify the changed parts and only update the affected cache items instead of clearing the entire cache.

[0079] Compressed storage: For large cache items, the system uses compression algorithms to reduce storage space usage, achieving a balance between storage efficiency and access speed.

[0080] Adaptive Optimization System (refer to Figure 1 106) is another core innovation of the present invention. It continuously monitors the execution of functions, including module loading time, function execution time, memory usage, etc., and uses machine learning algorithms to dynamically adjust various parameters and strategies of the system.

[0081] The core of the adaptive system is a decision engine based on reinforcement learning. The engine abstracts the execution environment of the Serverless function into a Markov decision process (MDP), where the state includes the current load situation, resource utilization, etc., and the actions include adjusting the preloading strategy, modifying the cache configuration, changing the memory allocation, etc. The reward function is calculated based on the execution time, resource utilization, and cost of the function.

[0082] The decision engine uses an improved Q-learning algorithm to learn the optimal strategy. In order to accelerate the learning process and adapt to the characteristics of the Serverless environment, the system also introduces the following innovations: Experience replay: store and reuse past experience to improve learning efficiency; Priority sampling: Give higher sampling probability to important experiences to accelerate the learning of key decisions; Model prediction: Combined with lightweight prediction models, it can quickly converge to a better strategy in limited attempts.

[0083] An important application of adaptive systems is to dynamically adjust module preloading strategies. Based on historical execution data and the current environment status, the system can intelligently decide which modules should be preloaded and which should be delayed. This dynamic adjustment greatly improves resource utilization efficiency while ensuring fast function response.

[0084] Another key application is to optimize memory management. The system dynamically adjusts garbage collection and memory allocation strategies by analyzing object lifecycles and access patterns. For example, for objects with short lifecycles, the system may choose a more aggressive recycling strategy; for large objects that exist for a long time, a special memory pool may be used to manage them.

[0085] The code cache optimizer and the adaptive optimization system work closely together to form a closed-loop optimization process. The cache system provides rich performance data to the adaptive system, while the adaptive system guides the adjustment of the cache strategy. This synergy ensures that the entire system can continue to evolve and adapt to different workloads and environmental changes.

[0086] This innovative caching and adaptive optimization method brings significant performance improvements. It not only greatly reduces the execution time of functions, but also improves resource utilization efficiency and reduces operating costs. This approach is particularly advantageous for Serverless applications with complex dependencies and changing workloads.

[0087] Embodiment 5: State preservation and fast recovery.

[0088] This embodiment describes in detail the implementation details of the state preservation and fast recovery module, which is a key innovation in the present invention, and aims to further reduce the cold start time of the Python Serverless function and improve the performance stability of the function between consecutive calls.

[0089] The core idea of ​​the state preservation and fast recovery module is to save the key states of the Python interpreter after the function is executed, and quickly rebuild these states when the next call is made, thus avoiding a lot of repeated initialization work. This module mainly includes two key components: state snapshot generator and state recovery engine.

[0090] The state snapshot generator is responsible for creating a lightweight but comprehensive runtime state snapshot at the end of function execution. This snapshot includes a list of loaded modules and their memory addresses, the values ​​of global variables and constants, the memory layout of function objects, the state of static objects, and key configuration parameters of the interpreter. In order to generate this snapshot, the system implements an innovative memory traversal algorithm. This algorithm can intelligently identify and serialize key memory structures while avoiding saving unnecessary temporary data. In particular, for large objects, the system only saves their metadata and memory layout instead of the complete data content, which greatly reduces the size of the snapshot.

[0091] The state snapshot is stored in a custom binary format that is optimized for fast serialization and deserialization. At the same time, the system also implements incremental snapshot technology, which only saves the parts that have changed relative to the previous snapshot, further reducing storage and transmission overhead.

[0092] The state recovery engine is responsible for quickly rebuilding the previously saved state when the function is called again. This process includes innovative steps such as fast memory mapping, deferred object reconstruction, pointer remapping, and incremental state updates. Fast memory mapping uses mmap technology to quickly load the saved state into memory. The deferred object reconstruction strategy completes the full reconstruction process only when the object is actually accessed. The pointer remapping algorithm ensures that all internal references remain correct when the memory address changes. Incremental state updates can intelligently identify the parts that need to be rebuilt while retaining other unaffected states.

[0093] In order to adapt to the particularity of the Serverless environment, this module also implements innovative features such as state compression, security protection, version control, and adaptive strategies. State compression uses a specially designed algorithm to minimize storage space while ensuring fast decompression. The security protection mechanism ensures that the saved state does not leak sensitive information. Version control ensures that each state snapshot is associated with a specific version of the function code to ensure the consistency of the state and code. The adaptive strategy dynamically evaluates the cost-effectiveness of state preservation and recovery, and may choose not to save the state in some cases to optimize overall performance.

[0094] The state preservation and fast recovery module is tightly integrated with other modules. It works with the layered module loader to ensure that the fast-recovered state is compatible with the lazy loading strategy. It also provides important performance indicators for the adaptive optimization system, helping the system make smarter optimization decisions.

[0095] This innovative state preservation and fast recovery mechanism brings significant performance improvements. In typical scenarios, it can significantly reduce the cold start time of Python Serverless functions. For applications with high initialization overhead, the performance improvement is even more significant. In addition, this mechanism also improves the performance stability of functions in high-concurrency scenarios. By quickly restoring the pre-initialized state, the system can better cope with sudden large numbers of requests and maintain consistent response time.

[0096] Example 6: JIT compilation and memory management optimization.

[0097] This example describes the implementation details of the JIT (Just-in-Time) compilation enhancer and memory management optimizer. These two components work together to further improve the execution efficiency and resource utilization of Python Serverless functions.

[0098] The JIT compiler enhancer is an important innovation of this system. It significantly improves the execution speed of functions by compiling hot code into machine code in real time. Unlike traditional Python JIT compilers, this system is optimized specifically for the characteristics of the Serverless environment.

[0099] The JIT compiler uses an innovative tiered compilation strategy. The first tier is lightweight bytecode optimization, which starts working when the function is first executed, quickly performing basic optimizations on the bytecode, such as constant folding and dead code elimination. The second tier is trace-based JIT compilation, which identifies hot execution paths and compiles these paths into highly optimized machine code.

[0100] In order to adapt to the short execution time of Serverless functions, the JIT compiler adopts a more aggressive optimization strategy. It lowers the compilation threshold and considers compilation even for code segments with fewer execution times. At the same time, the compiler uses an innovative speculative optimization technology to predict possible execution paths based on historical execution data and optimize these paths in advance.

[0101] The JIT compiler also implements a mechanism to save compilation results across calls. The compiled machine code will be serialized and stored, and can be quickly loaded and used the next time the function is called. This greatly reduces the overhead of repeated compilation and improves cold start performance.

[0102] The memory management optimizer is another key component. It implements multiple innovative memory management strategies based on the characteristics of the Serverless environment.

[0103] First, the system implements a dedicated short-lifetime object allocator. This allocator uses a pre-allocated memory pool and a fast recycling strategy to greatly reduce the overhead of frequently creating and destroying small objects. It also implements an intelligent memory layout optimization to store related small objects in a centralized manner and improve cache hit rates.

[0104] Secondly, the system optimizes the garbage collection (GC) algorithm. The traditional Python GC algorithm may cause long pause times in the Serverless environment. To solve this problem, the system implements an incremental and concurrent GC algorithm. This algorithm distributes the GC process into multiple small time slices and runs concurrently with the main program as much as possible, thereby greatly reducing the GC pause time.

[0105] Another innovation is the dynamic memory limit mechanism. The system monitors memory usage in real time and dynamically adjusts memory limits based on the execution mode of the function. For operations that require a large amount of memory in a short period of time, the system will temporarily relax the limit; during idle periods, the limit will be tightened to free up resources.

[0106] The JIT compiler enhancer and memory management optimizer work closely together. The JIT compiler generates memory-friendly code, such as optimizing object layout to reduce memory usage, or inserting prefetch instructions to improve memory access efficiency. At the same time, the memory management optimizer provides the JIT compiler with feedback on memory usage to help the compiler make better optimization decisions.

[0107] These two components are also integrated with other parts of the system. For example, they exchange performance data with the adaptive optimization system to help the system adjust the optimization strategy as a whole. At the same time, they also cooperate with the state preservation and restoration module to ensure that the optimized code and optimized memory layout can be effectively saved and quickly restored between function calls.

[0108] Through these innovative JIT compilation and memory management technologies, the system can significantly improve the execution efficiency of Python Serverless functions. Especially for compute-intensive functions, the performance improvement is more obvious. At the same time, the optimized memory management strategy also enables the system to more effectively utilize limited Serverless environment resources and support larger-scale concurrent execution.

[0109] Embodiment 7: Security mechanism and API integration.

[0110] This embodiment describes in detail the implementation details of the security mechanism and the API integration module. These two components jointly ensure the security, scalability and ease of use of the system, and are the key support for the present invention in practical applications.

[0111] The security mechanism is an important innovation of this system. It implements a multi-level security protection strategy for the special needs of the Serverless environment. The first is the module-level isolation mechanism. The system uses an innovative sandbox technology to create an independent execution environment for each function instance. This sandbox not only isolates the running space of different function instances, but also implements fine-grained resource access control.

[0112] The core of sandbox technology is a lightweight virtualization layer that implements isolation by hooking system calls and rewriting sensitive operations. Compared with traditional containerized isolation, this method has lower overhead and faster startup speed, which is very suitable for the needs of Serverless environment. At the same time, the system implements a dynamic permission adjustment mechanism, which can adjust the permissions of functions in real time according to their behavior, further enhancing security.

[0113] Another key security feature is code integrity verification. The system uses a combination of digital signatures and hash checks to ensure that the executed code has not been tampered with. In particular, for precompiled modules and cached states, the system performs integrity checks each time they are loaded to prevent potential code injection attacks.

[0114] In order to deal with potential information leakage risks, the system also implements a sensitive data encryption mechanism. All state snapshots and compilation results stored in persistent storage are encrypted. Encryption uses an innovative lightweight encryption algorithm, which minimizes the performance overhead caused by encryption and decryption while ensuring security.

[0115] The version control system is another important component of the security mechanism. It not only tracks the version of the function code, but also maintains the version information of all related components (such as precompiled modules, cache status, etc.). This ensures the consistency of all components in the system and prevents security vulnerabilities that may be caused by version mismatches.

[0116] The API integration module focuses on providing a flexible and easy-to-use interface so that developers can fully utilize the system's optimization capabilities. This module has designed a comprehensive set of APIs that allow developers to finely control the system's functions.

[0117] The core API includes module preloading configuration, lazy loading strategy settings, cache strategy customization, etc. For example, developers can use the API to specify which modules need to be preloaded and which can be lazy loaded. The system will automatically adjust the loading strategy based on these configurations.

[0118] The API also provides performance monitoring and diagnostic capabilities. Developers can obtain real-time function execution statistics, including loading time, execution time, memory usage, etc. This information is crucial for optimizing function performance.

[0119] To simplify the integration process, the system provides a series of preset configuration templates. These templates are optimized for different types of application scenarios (such as Web API, data processing, machine learning reasoning, etc.), and developers can use them directly or customize them based on these templates.

[0120] The API integration module also implements an adaptation layer for mainstream Serverless platforms. This adaptation layer abstracts the differences between different platforms and provides a unified interface. This allows developers to easily deploy optimized functions to different Serverless environments without modifying the code.

[0121] One innovation is the dynamic configuration update mechanism. The system allows dynamic adjustment of optimization strategies at runtime through APIs without redeploying functions. This greatly improves the flexibility of the system, allowing developers to optimize function performance in real time based on actual operating conditions.

[0122] The security mechanism and API integration module work closely together. All operations performed through the API will undergo security checks to ensure that the isolation and integrity of the system are not compromised. At the same time, the API also provides security-related configuration options, allowing developers to adjust security policies according to their needs.

[0123] These two modules are also deeply integrated with other parts of the system. For example, the performance data collected by the API is fed back to the adaptive optimization system to help it make smarter decisions. The security mechanism collaborates with the state preservation and recovery module to ensure the security of the stored state information.

[0124] Through these innovative security mechanisms and flexible APIs, the system not only ensures security in the Serverless environment, but also provides great customizability and ease of use. This allows developers to fully tap the optimization potential of the system while ensuring security and create high-performance, high-reliability Serverless applications.

[0125] It should be noted that although this embodiment is mainly aimed at the Python language and a specific Serverless platform, the core ideas and methods can be extended to other programming languages ​​and platforms. The specific implementation may need to be adjusted according to the characteristics of different environments. The scope of protection of the present invention is not limited to the above-mentioned specific implementation methods, but should include various equivalent changes and modifications. For those skilled in the art, many improvements and variations can be made without violating the principles of the present invention, all of which belong to the scope of protection of the present invention. For example, the present invention can be combined with technologies such as virtualization and edge computing to further expand the application scenarios and effects of intelligent scheduling.

Claims

1. A system for optimizing the cold start time of Python serverless functions, characterized in that: The system includes: The dependency analysis module is used to analyze the function code and its dependencies during the function deployment phase and generate a dependency graph; Module precompilation system, used to precompile Python source code into optimized bytecode or machine code; Hierarchical module loader, used to load modules at different levels according to predefined priorities; A lazy import mechanism to delay the actual loading and initialization of modules; Code cache optimizer, used to cache the status of hot functions, frequently used objects and loaded modules; An adaptive optimization system that monitors module loading, code execution, and memory usage, and dynamically adjusts preloading and caching strategies.

2. The system according to claim 1, characterized in that The dependency analysis module also includes a dynamic tracking unit for recording the modules actually imported and the order of use during runtime.

3. The system according to claim 1, characterized in that The module precompilation system supports incremental compilation, compiling only the changed parts, and generates optimized code specific to the target serverless environment.

4. The system according to claim 1, characterized in that The hierarchical module loader divides modules into a core layer, a function layer, a library layer and a dynamic layer, and loads them according to hierarchical priority.

5. The system according to claim 1, characterized in that The lazy import mechanism consists of a custom import hook that creates a placeholder object when an import statement is encountered, triggering the actual loading and initialization only when the module is actually used.

6. The system according to claim 1, characterized in that It also includes a JIT compilation enhancer to integrate or improve the existing Python JIT compiler, optimize the JIT strategy for the serverless environment, and support saving JIT compilation results across calls.

7. The system according to claim 1, characterized in that It also includes a state preservation and fast recovery module, which is used to save the key state of the Python interpreter after the function is executed and quickly rebuild the interpreter state in a new call.

8. The system according to claim 1, characterized in that Also included is a memory management optimizer that implements special memory allocation strategies for short-lived functions and optimizes garbage collection algorithms to reduce GC pause times.

9. The system according to claim 1, characterized in that It also includes security mechanisms and API integration modules. The security mechanism is used to implement module-level isolation and version control. The API integration module is used to provide an API that allows developers to specify preloaded or lazy-loaded modules and design integration solutions with mainstream Python serverless platforms.

Images