System and method for optimizing. NET Serverless function cold start time

Abstract

The invention provides a system and a method for optimizing the cold start time of a. NET Serverless function. The system comprises a dependency analysis module, an AOT compiling optimization module, a dynamic code optimization module, a program set loading optimization module, a type system optimization module, a memory management optimization module, a state storage and recovery module and an adaptive optimization system. Dependence analysis is carried out through static analysis and dynamic tracking, partial AOT compiling and self-adaptive JIT compiling are achieved, program set loading and a type system are optimized, a special memory allocation strategy and lightweight state serialization are achieved, and the strategy is continuously monitored and dynamically adjusted and optimized. The method further comprises the steps of asynchronous operation optimization and security assurance. The method has the advantages that the cold start time of the. NET Serverless function is remarkably shortened, the function execution efficiency is improved, meanwhile, cross-platform compatibility and safety are guaranteed, and a more efficient solution is provided for Serverless calculation.

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 execution performance of .NET Serverless functions. Background Art

[0002] As an emerging model of cloud computing, serverless computing has been widely used in recent years. It allows developers to focus on code logic without worrying about the underlying infrastructure, which greatly improves development efficiency and resource utilization. In the serverless architecture, function as a service (FaaS) is an important implementation form, and .NET, as one of the mainstream development frameworks, has important applications in the serverless field. However, a key challenge facing .NET serverless functions is the long cold start time, which directly affects user experience and system performance.

[0003] At present, the main solutions to solve the cold start problem include preheating instances, optimizing dependency loading, and using lightweight runtimes. Preheating instance technology keeps functions active by triggering them regularly, thereby reducing the probability of cold starts. Optimizing dependency loading uses static analysis and dynamic tracking to streamline the dependencies required for functions and reduce loading time. Using lightweight runtimes, such as .NET Core, can shorten startup time to a certain extent. In addition, some cloud service providers also provide specific optimization measures, such as Azure's App Service preheating function.

[0004] However, these solutions still have some problems. Although preheating instances can reduce the occurrence of cold starts, it will increase resource consumption and costs. Although optimizing dependency loading is effective, it often requires manual intervention by developers, which increases development complexity. Using a lightweight runtime may limit the use of certain features and affect the flexibility of the application. In addition, most of these methods are aimed at general scenarios and it is difficult to fully utilize the features of the .NET runtime to achieve deeper optimization.

[0005] To solve the above problems, some studies have proposed more radical optimization strategies, such as using AOT (Ahead-of-Time) compilation to replace JIT (Just-in-Time) compilation, or implementing persistence of some runtime states. Although these methods have improved cold start performance to a certain extent, they often require deep customization of the .NET runtime, which is highly complex to implement and may affect cross-platform compatibility and dynamic characteristics. At the same time, these methods usually lack adaptive capabilities and it is difficult to dynamically adjust the optimization strategy according to the characteristics of different functions and the operating environment.

[0006] Therefore, there is an urgent need for a system and method that can fully optimize the cold start time of .NET Serverless functions, which can fully utilize the characteristics of the .NET runtime while maintaining good compatibility and flexibility. The system should be able to automatically analyze and optimize function dependencies, implement intelligent compilation and loading strategies, and have adaptive capabilities to adapt to different operating environments and load characteristics. Developing such an optimization system will not only significantly improve the performance of .NET Serverless functions, but will also lay the foundation for the application of Serverless computing in a wider range of fields. Summary of the invention

[0007] The main purpose of the present invention is to solve the problem of long cold start time of existing .NET Serverless functions and improve function response speed and resource utilization efficiency. In addition, the present invention also aims to provide an adaptive optimization system that can dynamically adjust the optimization strategy according to different function characteristics and operating environments while maintaining good cross-platform compatibility and security.

[0008] To achieve the above objectives, the present invention provides a system and method for optimizing the cold start time of .NET Serverless functions, which is characterized by comprising a dependency analysis module, an AOT compilation optimization module, a dynamic code optimization module, an assembly loading optimization module, a type system optimization module, a memory management optimization module, a state preservation and recovery module, and an adaptive optimization system. In some embodiments, the system also includes an asynchronous operation optimization module and a security mechanism module to further improve performance and ensure security.

[0009] Specifically, the dependency analysis module generates an accurate dependency graph through static analysis and dynamic tracing, providing a basis for subsequent optimization.

[0010] Furthermore, the AOT compilation optimization module implements partial AOT compilation, optimizes compilation for critical paths and hot code, and supports cross-platform AOT compilation to adapt to different serverless environments.

[0011] Preferably, the dynamic code optimization module adopts an adaptive JIT compilation strategy, dynamically adjusts the compilation threshold according to the function call mode, and implements a code cache mechanism for subsequent call reuse.

[0012] Optionally, the assembly loading optimization module includes an intelligent preloading system and a lightweight assembly loader, and implements an assembly cache mechanism to reduce the overhead of repeated loading and parsing.

[0013] In one embodiment, the type system optimization module implements a type warming mechanism and a generic specialization technique, and includes a value type layout optimization function to reduce boxing operations and memory allocations.

[0014] In certain embodiments, the memory management optimization module implements a special memory allocation strategy for short lifecycle functions and includes an optimized garbage collection algorithm and object pool mechanism to reduce GC pause time and reuse common object structures.

[0015] In addition, the present invention can also implement a lightweight runtime state serialization and fast recovery mechanism through a state preservation and recovery module, including an incremental state update function, so as to only preserve and restore the changed parts.

[0016] In other embodiments, the adaptive optimization system continuously monitors assembly loading, code execution, and memory usage, dynamically adjusts optimization strategies, and includes a self-learning mechanism to continuously optimize prediction models and compilation strategies.

[0017] In a preferred embodiment, the system further includes an asynchronous operation optimization module for designing an efficient asynchronous state machine, optimizing the async / await mode, and implementing a batch processing mechanism for asynchronous operations to further improve function execution efficiency.

[0018] In another preferred embodiment, the security mechanism module implements application domain-level isolation, code access security enhancement mechanism, and dynamic code audit system to ensure that security and isolation are not affected while optimizing performance.

[0019] By adopting the above scheme, the present invention has the following beneficial effects: (1) Significantly reduce the cold start time of .NET Serverless functions and improve function response speed; (2) Through adaptive optimization strategies, intelligent adaptation to different types of functions and operating environments is achieved; (3) Maintain good cross-platform compatibility and adapt to different serverless environments; (4) Ensure the security and isolation of function execution while optimizing performance; (5) Improve resource utilization efficiency and reduce the operating costs of serverless applications.

[0020] In summary, the present invention not only solves the problem of long cold start time of .NET Serverless functions, but also provides a comprehensive optimization system that can adaptively improve function execution efficiency, laying a solid foundation for the application of Serverless computing in a wider range of fields. 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 It is the basic architecture diagram of the system. It shows the core modules of the entire optimization system and their interrelationships.

[0023] Figure 2 This is a workflow diagram of the dependency analysis module. It describes the various steps of dependency analysis, from code parsing to data aggregation.

[0024] Figure 3 It is a diagram of the layered AOT compilation strategy. It shows the compilation strategy where the code is divided into the core layer, the middle layer, and the peripheral layer.

[0025] Figure 4 It is a diagram of the prediction execution engine. It describes the cycle of data collection, model training, and prediction execution.

[0026] Figure 5 This is a diagram of the type warm-up mechanism. It shows the type warm-up process from static analysis to background initialization of minor types.

[0027] Figure 6 is a regionalized memory manager diagram. It describes the relationship between memory regions, object allocators, garbage collectors, and object lifecycle predictions.

[0028] Figure 7 This is a diagram of the incremental state snapshot mechanism. It shows the cycle of state tracking, difference analysis, serialization, storage, and reconstruction.

[0029] Figure 8 It is a multi-dimensional performance evaluation model diagram. It describes how different performance indicators are input into the performance evaluation and affect the dynamic optimization strategy selection.

[0030] Fig. 9 is a diagram of the smart asynchronous scheduler. It shows the main components of the asynchronous scheduler and their relationships.

[0031] Fig.10 is a diagram of the dynamic security policy enforcer. It describes the main components of the security policy enforcer. Fig.11 is the optimization configuration interface diagram. It shows the main components of the optimization configuration interface and their relationship with the API user. DETAILED DESCRIPTION

[0032] 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.

[0033] 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.

[0034] Example 1: Basic system architecture.

[0035] This embodiment describes in detail the basic architecture of a system for optimizing the cold start time of .NET Serverless functions. Figure 1 As shown, the system includes the following core modules: dependency analysis module (101), AOT compilation optimization module (102), dynamic code optimization module (103), assembly loading optimization module (104), type system optimization module (105), memory management optimization module (106), state preservation and recovery module (107) and adaptive optimization system (108).

[0036] The dependency analysis module (101) is the foundation of the system. It generates an accurate dependency graph through static analysis and dynamic tracing. Static analysis is performed during the function deployment phase, and the initial dependency graph is constructed by parsing the function code and the referenced assemblies. Dynamic tracing is performed when the function is running, recording the actual loaded assemblies and execution paths. This dual analysis method can capture dynamically loaded dependencies that may be missed by traditional static analysis, thereby providing more accurate information for subsequent optimization.

[0037] The AOT compilation optimization module (102) uses the results of dependency analysis to implement an innovative partial AOT compilation strategy. Unlike traditional full AOT compilation, this module only performs AOT compilation on critical paths and hot code. This strategy determines compilation priority by considering function call frequency, code complexity, and execution time ratio. In this way, the system can maximize the performance improvement brought by AOT compilation while maintaining flexibility.

[0038] The dynamic code optimization module (103) implements an adaptive JIT compilation strategy. The module dynamically adjusts the JIT compilation threshold by monitoring the function call pattern. Specifically, it uses an adaptive algorithm to predict the execution frequency and duration of the function. Based on this prediction, the system can intelligently decide when to trigger JIT compilation and the optimization level of the compilation.

[0039] The assembly loading optimization module (104) includes an intelligent preloading system and a lightweight assembly loader. The intelligent preloading system preloads the most likely assembly to be used before the function is cold started based on the result of dependency analysis. The lightweight assembly loader reduces the loading time by loading only the necessary type information. The combination of the two significantly reduces the delay caused by assembly loading.

[0040] The type system optimization module (105) implements a type preheating mechanism and a generic specialization technique. The type preheating mechanism pre-initializes commonly used types during the function initialization phase, reducing the type initialization overhead at runtime. The generic specialization technique generates dedicated code for commonly used generic type instances, avoiding the generic code generation overhead at runtime.

[0041] The memory management optimization module (106) implements a special memory allocation strategy for the short life cycle of Serverless functions. It uses a region-based memory allocator to concentrate memory allocation operations in a pre-allocated memory pool, greatly reducing the memory allocation overhead. At the same time, the module also implements an optimized garbage collection algorithm to improve the responsiveness of the function by reducing the GC pause time.

[0042] The state preservation and recovery module (107) implements a lightweight runtime state serialization and fast recovery mechanism. It uses an incremental state update technology to serialize and restore only the changed state, thereby minimizing the overhead of state preservation and recovery. This is especially important for Serverless functions that need to be started and stopped frequently.

[0043] The adaptive optimization system (108) is the core control unit of the entire system. It continuously monitors the performance indicators of each module, such as assembly loading time, code execution time, memory usage, etc., and dynamically adjusts the optimization strategy based on these indicators. The system also includes a self-learning mechanism that can continuously optimize its decision model based on historical data.

[0044] These modules communicate and collaborate through a central coordinator (not shown in the figure). The coordinator is responsible for scheduling the work of each module and ensuring that they can work together to achieve the best optimization effect.

[0045] The innovation of this embodiment is that it is not just a simple combination of existing technologies, but a series of special optimization strategies are designed through an in-depth understanding of the characteristics of the .NET runtime and the Serverless environment. For example, some AOT compilation strategies and adaptive JIT compilation strategies are designed for the special execution mode of Serverless functions. At the same time, the incremental state update technology is also developed for the frequent start and stop characteristics of Serverless functions. These innovative designs enable this system to significantly improve the performance of Serverless functions while maintaining the flexibility of the .NET runtime.

[0046] Example 2: Dependency analysis and AOT compilation optimization.

[0047] This embodiment describes in detail the specific implementation and working principle of the dependency analysis module and the AOT compilation optimization module. The two modules work closely together to provide a solid foundation for the optimization of the entire system.

[0048] The dependency analysis module uses an innovative method that combines static analysis and dynamic tracing to generate an accurate dependency graph. Figure 2 As shown in the figure, the static analysis phase first uses the .NET Compiler Platform (Roslyn) API to perform syntax and semantic analysis on the function source code, then scans the project files and assembly references to identify direct dependencies, and finally recursively analyzes each direct dependency to build a complete static dependency graph. The dynamic tracing phase inserts tracing code at the function entry point and key methods, records the actual loaded assembly, executed code path, and accessed type at runtime, and finally merges the data collected at runtime with the static analysis results to generate a comprehensive dependency graph.

[0049] A key innovation of the dependency analysis module is the introduction of the concept of "dependency weight". Each dependency is assigned a weight value that reflects its importance in function execution. The weight calculation takes into account factors such as usage frequency, execution time share, and loading overhead. This weighted dependency graph provides precise guidance for subsequent AOT compilation optimization.

[0050] The AOT compilation optimization module uses the results of dependency analysis to implement an innovative "layered AOT compilation" strategy. Figure 3 As shown in Figure 1, this strategy divides the code into core, middle, and peripheral layers. The core layer contains the most critical and most frequently used code paths, all of which are AOT compiled; the middle layer is selectively compiled based on dependency weights, using a hybrid of partial AOT and partial JIT; the peripheral layer maintains JIT compilation to ensure maximum flexibility.

[0051] During the AOT compilation process, the module uses technologies such as code trimming, inline optimization, specialization optimization, and cross-module optimization. Code trimming removes unused code paths based on dependency analysis results to reduce the size of compiled output. Inline optimization actively inlines methods during compilation to reduce method call overhead at runtime. Specialization optimization generates specialized non-generic versions for frequently used generic types. Cross-module optimization breaks traditional module boundaries and performs global code optimization.

[0052] To support cross-platform deployment, the AOT compilation module also implements "adaptive binary generation" technology. It can dynamically adjust the compilation strategy and optimization level according to the characteristics of the target deployment environment (such as CPU architecture, operating system), and generate the most suitable local code for a specific environment.

[0053] A feedback loop is established between the AOT compilation optimization module and the dynamic code optimization module. The performance data collected by the dynamic optimization module during runtime is regularly fed back to the AOT compilation module for adjusting compilation strategies and optimization decisions. This dynamic and static approach ensures that the system can continuously optimize itself and adapt to the actual operating characteristics of the function.

[0054] The innovation of this embodiment is mainly reflected in the introduction of the concept of weighted dependency graph, the implementation of layered AOT compilation strategy, the development of adaptive binary generation technology, and the establishment of a feedback mechanism between AOT compilation and dynamic optimization. These innovations not only significantly reduce the cold start time of .NET Serverless functions, but also improve the overall execution efficiency of functions. At the same time, due to the use of layered compilation and adaptive optimization strategies, the system maintains good flexibility and can adapt to different deployment environments and operating scenarios.

[0055] It should be noted that the effect of AOT compilation optimization may vary depending on the complexity and dependency structure of the function. For functions with fewer dependencies and simple logic, the optimization effect may not be as obvious as for complex functions. In addition, the initial compilation may increase the deployment time, but this can usually be compensated by the improved function execution performance.

[0056] Embodiment 3: Dynamic code optimization and assembly loading optimization.

[0057] This example describes in detail the implementation principles and workflows of the dynamic code optimization module and the assembly loading optimization module. These two modules work together to further improve the runtime performance of .NET Serverless functions.

[0058] The dynamic code optimization module implements an adaptive JIT compilation strategy, the core of which is an innovative predictive execution engine. Figure 4As shown in Figure 1, the engine dynamically adjusts the JIT compilation strategy by continuously monitoring the calling patterns and execution characteristics of functions. The predictive execution engine uses an improved time series analysis algorithm that can accurately predict the calling frequency and execution time of functions. Based on these predictions, the system can intelligently decide when to trigger JIT compilation and what level of optimization to apply.

[0059] The workflow of the predictive execution engine includes three stages: data collection, model training, and predictive execution. In the data collection stage, the system records information such as the function call time, execution time, and resource consumption. The model training stage uses the collected historical data to update the predictive model. The predictive execution stage guides JIT compilation decisions based on the output of the model.

[0060] The dynamic code optimization module also implements an innovative code cache mechanism. This mechanism not only caches compiled local code, but also dynamically adjusts the cache strategy based on the execution characteristics of the function. For example, for functions that are frequently called but have a short execution time, the system will prioritize keeping their compiled code in the cache to reduce the overhead of repeated compilation.

[0061] The assembly loading optimization module includes an intelligent preloading system and a lightweight assembly loader. The intelligent preloading system preloads the most likely assemblies before the function is cold started based on the weighted dependency graph generated by the dependency analysis module. This process is dynamic and the preloading strategy will be continuously adjusted according to the actual execution of the function.

[0062] The lightweight assembly loader is another innovation of this module. It reduces loading time by loading only necessary type information. Specifically, the loader implements a "delayed metadata resolution" technique. When the assembly is first loaded, only the most basic metadata information is parsed, and detailed type information parsing is postponed until it is actually used. This approach significantly reduces the initial loading time, especially for large assemblies.

[0063] To further optimize assembly loading, this module also implements an assembly cache mechanism. This mechanism not only caches loaded assemblies, but also predicts and preloads assemblies that may be needed soon. The cache strategy uses an improved LRU (Least Recently Used) algorithm, taking into account factors such as assembly usage frequency, size, and loading overhead.

[0064] A collaborative working mechanism is established between the dynamic code optimization module and the assembly loading optimization module. For example, when the assembly loading optimization module detects that an assembly is frequently loaded, it will notify the dynamic code optimization module to consider precompiling the key methods in the assembly. Conversely, when the dynamic code optimization module finds that certain code paths are frequently executed, it will also notify the assembly loading optimization module to adjust the preloading strategy of the relevant assembly.

[0065] The main innovations of this embodiment include: (1) The design of the predictive execution engine makes JIT compilation decisions more intelligent and dynamic; (2) Improved code caching mechanism improves code reuse efficiency; (3) Delayed metadata parsing technology significantly reduces the initial loading time of assemblies; (4) The intelligent preloading system and assembly cache mechanism work together to optimize the overall loading performance.

[0066] These innovations significantly improve the runtime performance of .NET Serverless functions, especially in complex application scenarios. Through dynamic optimization and intelligent loading, the system can adapt to different function calling modes and execution environments, maximizing performance advantages while maintaining the flexibility of the .NET runtime.

[0067] It is important to note that the effects of dynamic optimization and smart loading may vary with the complexity of the function and the change of the calling pattern. The system needs a certain amount of learning time to adapt to the new execution mode, so there may be short-term performance fluctuations when the function behavior changes significantly.

[0068] Embodiment 4: Type system optimization and memory management optimization.

[0069] This example describes the implementation details and working principles of the type system optimization module and the memory management optimization module. These two modules work together to further optimize the operating efficiency and resource utilization of .NET Serverless functions.

[0070] The type system optimization module implements the type preheating mechanism and generic specialization technology. The core idea of ​​the type preheating mechanism is to pre-initialize common types during the function initialization phase, thereby reducing the type initialization overhead at runtime. Figure 5 As shown, the mechanism includes the following steps: First, the most commonly used types in a function are identified through static analysis and historical execution data. Then, during the function cold start phase, the system preloads metadata for these types and executes the necessary static constructors. This process is incremental, meaning that the system continues to initialize secondary types in the background without blocking the main execution flow.

[0071] One of the innovations of the type warming mechanism is its adaptability. The system continuously monitors the usage of types and dynamically adjusts the warming list. This ensures that the warming process can adapt to changes in function behavior and always focus on the most critical types.

[0072] Generic specialization technology generates specialized code for commonly used generic type instances. The core of this technology is an intelligent code generator that can identify frequently used generic type instances in functions and generate non-generic specialized versions for them. This not only reduces runtime boxing / unboxing operations, but also improves code execution efficiency.

[0073] The generic specialization process is dynamic. The system will continuously evaluate the usage frequency of generic types based on the actual execution of the function and trigger the generation of specialized code at the appropriate time. In order to balance performance and memory usage, the system also implements a specialized code caching mechanism to retain only the most commonly used specialized versions.

[0074] The memory management optimization module implements a special memory allocation strategy and optimized garbage collection algorithm based on the short life cycle of Serverless functions. Its core is an innovative regional memory manager, such as Figure 6 shown.

[0075] The regionalized memory manager divides the memory into multiple "regions", each of which is responsible for allocating objects of a specific type or life cycle. The advantages of this approach are: reducing memory fragmentation and improving memory utilization. Simplifying the garbage collection process and reducing GC pause time. Supporting fast batch memory release, it is suitable for the execution model of Serverless functions.

[0076] The memory manager also implements an adaptive object lifecycle prediction algorithm. By analyzing the allocation and usage patterns of objects, the system can more accurately predict the lifecycle of objects, thereby optimizing memory allocation strategies and GC triggering timing.

[0077] The garbage collection algorithm has also been optimized. The system implements an incremental GC algorithm that distributes the GC process into multiple small time slices, significantly reducing the pause time caused by a single GC. At the same time, the GC triggering strategy has also been optimized to consider the execution phase of the function and the expected remaining running time, and try to avoid large-scale GC on the critical execution path.

[0078] A collaboration mechanism has been established between the type system optimization module and the memory management optimization module. For example, the type preheating process will pre-allocate memory for the preheated type to reduce the memory allocation overhead at runtime. Similarly, the code generated by the generic specialization technology will directly use the optimized memory allocation strategy to further improve execution efficiency.

[0079] The main innovations of this embodiment include: adaptive type preheating mechanism, dynamic optimization of type initialization process, intelligent generic specialization technology, reducing runtime overhead, regionalized memory manager, improving memory utilization efficiency, incremental GC algorithm and optimized GC triggering strategy, reducing execution interruption.

[0080] These innovations significantly improve the operating efficiency and resource utilization of .NET Serverless functions. By optimizing the type system and memory management, the system not only reduces the cold start time of functions, but also improves overall execution performance.

[0081] It should be noted that the effects of these optimization strategies may vary depending on the specific characteristics of the function. For functions with simpler type usage and stable memory allocation patterns, the optimization effect may not be as obvious as for complex functions. In addition, the system needs some time to learn and adapt to the behavior of the function during the first run, so there may be short-term performance fluctuations.

[0082] Embodiment 5: State preservation and recovery mechanism.

[0083] This example describes the design principle and implementation details of the state preservation and recovery module. This module aims to solve the state management problem of Serverless functions during frequent cold and hot starts, significantly improving the response speed and resource utilization efficiency of the function.

[0084] The core of the state preservation and recovery module is an innovative "incremental state snapshot" mechanism. Figure 7 As shown, the mechanism includes the following key components: State tracker: responsible for real-time monitoring of state changes during function execution.

[0085] Diff Analyzer: Identify differences in state between two executions.

[0086] Serialization engine: Efficiently serializes state differences into a compact binary format.

[0087] State Storage Manager: Manages the storage and retrieval of serialized state data.

[0088] State Reconstructor: Quickly rebuild previously saved state when a function starts.

[0089] The workflow of the incremental state snapshot mechanism is as follows: First, the state tracker continuously monitors changes in key states during function execution. This includes but is not limited to static field values, global caches, connection pool states, etc. The state tracker uses a low-overhead memory mapping technology that can efficiently capture state changes without significantly affecting the execution performance of the function.

[0090] Second, the difference analyzer compares the state at the end of the current execution with the state at the end of the previous execution and identifies the parts that have changed. This step uses an improved hashing algorithm to quickly locate state differences while minimizing memory and CPU overhead.

[0091] The serialization engine then converts the identified state differences into a highly optimized binary format. The innovation here is the use of a context-aware compression algorithm that dynamically adjusts the compression strategy based on the data type and change pattern, minimizing the time overhead of serialization and deserialization while ensuring a high compression rate.

[0092] Next, the state storage manager is responsible for storing the serialized state differences in persistent storage. Considering the characteristics of the Serverless environment, the manager implements a tiered storage strategy: small states that change frequently are stored in fast-access memory, while large or infrequently changing states are stored in persistent storage.

[0093] Finally, when the function needs to be restarted, the state reconstructor quickly rebuilds the function's runtime state based on the stored state differences. This process is incremental and parallel, that is, the system will prioritize rebuilding critical states while continuing to load secondary states in the background, thereby minimizing the startup time of the function.

[0094] Another innovation of this module is the "predictive state warm-up" mechanism. Based on the analysis of function call patterns, the system can predict the next possible function call and start the state reconstruction process in advance. This further reduces the response delay of the function, especially for functions with certain call patterns.

[0095] The state preservation and recovery module also establishes a collaboration mechanism with other modules. For example, it will coordinate with the type system optimization module to ensure that the state during the type warm-up process is also correctly saved and restored. Similarly, it will also collaborate with the memory management optimization module to optimize the memory layout of the state data to improve the efficiency of serialization and deserialization.

[0096] The main innovations of this embodiment include: an incremental state snapshot mechanism that achieves efficient state difference capture and recovery, and a context-aware serialization algorithm that balances compression rate and processing speed.

[0097] The tiered storage strategy adapts to the special needs of the Serverless environment. The predictive state warm-up mechanism further reduces function response latency.

[0098] These innovations significantly improve the startup performance and state management efficiency of .NET Serverless functions. Through intelligent state preservation and fast recovery, the system can minimize the time overhead of cold and hot starts while ensuring the correctness of function execution.

[0099] It should be noted that the effect of the state preservation and recovery mechanism may vary depending on the complexity and frequency of changes of the function's state. For functions with simple or infrequently changing states, the optimization effect may not be as obvious as for functions with complex and frequently changing states. In addition, the system needs a certain amount of time to learn and adapt to the state change pattern of the function, so some additional overhead may be required in the initial stage.

[0100] Example 6: Adaptive optimization system.

[0101] This embodiment describes in detail the design principle and implementation method of the adaptive optimization system. As the core control unit of the entire optimization framework, the system is responsible for coordinating the work of each optimization module and dynamically adjusting the optimization strategy according to the actual operation of the function.

[0102] The core of the adaptive optimization system is an innovative "multi-dimensional performance evaluation model". Figure 8 As shown in the figure, the model evaluates the function performance in real time from the following dimensions: (1) Startup time: including cold start and hot start time; (2) Execution efficiency: function execution speed and resource utilization; (3) Memory usage: memory allocation pattern and garbage collection efficiency; (4) State management: efficiency of state preservation and recovery; (5) Response delay: the speed at which a function responds to a request.

[0103] Based on this multi-dimensional evaluation model, the adaptive optimization system implements a "dynamic optimization strategy selector". This selector uses a machine learning algorithm to select the most suitable strategy for each optimization module based on the historical performance data of the function and the current operating environment.

[0104] The workflow of the adaptive optimization system is as follows: First, the system continuously collects runtime data of functions, including but not limited to execution time, memory usage, GC frequency, state changes, etc. These data are transmitted to the performance evaluation model in real time through a low-overhead telemetry mechanism.

[0105] Secondly, the multi-dimensional performance evaluation model analyzes the collected data to generate a performance profile of the function. This profile not only reflects the current performance status of the function, but also includes a prediction of the performance change trend.

[0106] Then, the dynamic optimization strategy selector selects the most suitable strategy for each optimization module based on the performance profile, combined with predefined optimization rules and machine learning models. For example, for functions with frequent changes in memory usage, the system may choose a more aggressive garbage collection strategy; and for compute-intensive functions, the system may increase the proportion of AOT compilation.

[0107] Finally, the system applies the selected optimization strategy to each module. This process is gradual, and the system monitors the performance impact of the strategy change and makes further adjustments as needed.

[0108] An important innovation of the adaptive optimization system is "scenario-aware optimization". The system can identify the running scenarios of functions (such as high concurrency, low latency, computationally intensive, etc.) and dynamically adjust the optimization strategy according to different scenarios. This ensures that the function can maintain optimal performance in various situations.

[0109] Another innovation is the "cross-function learning" mechanism. The system not only learns the optimization strategy of a single function, but also learns from the experience of other functions in the same or similar applications. This collective learning method significantly accelerates the learning speed and adaptability of the system.

[0110] The adaptive optimization system also implements an "optimization conflict resolver" that automatically balances these goals against the performance requirements of the function to find the best compromise when there are conflicts between different optimization goals (for example, reducing memory usage may increase execution time).

[0111] The main innovations of this embodiment include: a multi-dimensional performance evaluation model to comprehensively evaluate function performance; a dynamic optimization strategy selector to intelligently select the best optimization strategy; scenario-aware optimization to adapt to different operating environments and requirements; a cross-function learning mechanism to accelerate the learning and adaptation of optimization strategies; and an optimization conflict resolver to balance multiple optimization objectives.

[0112] These innovations enable the entire optimization system to intelligently adapt to the characteristics and requirements of various .NET Serverless functions and continuously provide the best performance optimization. The system can not only handle the optimization of a single function, but also coordinate the performance of multiple functions on a larger scale to achieve overall optimization.

[0113] It is important to note that the effects of the adaptive optimization system may only become apparent over time. In the initial stage, the system needs some time to learn and adapt to the behavior of the function. In addition, for functions whose behavior patterns change frequently, the system may need longer time to stabilize the optimization strategy.

[0114] This embodiment is the core of the entire .NET Serverless function optimization framework. It organically combines all the optimization modules described above to form an adaptive and intelligent optimization ecosystem. In this way, the present invention not only solves a single optimization problem, but also provides a comprehensive and sustainable optimization solution.

[0115] Embodiment 7: Asynchronous operation optimization and security mechanism.

[0116] This example describes in detail the design principles and implementation methods of the asynchronous operation optimization module and the security mechanism module. These two modules further improve the performance and security of .NET Serverless functions and are important components of the entire optimization system.

[0117] The core of the asynchronous operation optimization module is an innovative "intelligent asynchronous scheduler". Fig. 9 As shown, the scheduler contains the following key components: Asynchronous Pattern Analyzer: Identify asynchronous operation patterns in functions.

[0118] Coroutine optimizer: optimizes the underlying implementation of asynchronous operations.

[0119] Batch Aggregator: Aggregates multiple small asynchronous operations into batches.

[0120] Resource predictor: predicts the resources required for asynchronous operations and prepares them in advance.

[0121] The workflow of the intelligent asynchronous scheduler is as follows: First, the Asynchronous Pattern Analyzer identifies asynchronous operation patterns in functions through static code analysis and runtime monitoring. It can discover potential optimization opportunities such as parallelization possibilities, repeated patterns, etc.

[0122] Secondly, the coroutine optimizer optimizes the underlying asynchronous implementation based on the analysis results. It adopts a lightweight coroutine model, which significantly reduces the context switching overhead in the traditional async / await mode. This optimization is particularly suitable for a large number of micro-operation scenarios in the Serverless environment.

[0123] The batch aggregator then identifies asynchronous operations that can be batched and aggregates them. For example, multiple small database queries might be combined into a single batch query, reducing the number of network round trips and overall latency.

[0124] Finally, the resource predictor predicts the resources that asynchronous operations may need based on historical data and the current context. This allows the system to prepare the required resources in advance, further reducing latency.

[0125] Another innovation of the asynchronous operation optimization module is the "adaptive concurrency control" mechanism. This mechanism can dynamically adjust the concurrency according to the execution environment and load of the function. This not only improves resource utilization, but also avoids performance degradation caused by excessive concurrency.

[0126] The security mechanism module implements a multi-level security protection system. Its core is a "dynamic security policy executor", such as Fig.10 As shown, it contains the following key components: (1) Code integrity verifier: ensures that the executed code has not been tampered with; (2) Runtime Permission Manager: dynamically manages and adjusts the execution permissions of code; (3) Resource access monitor: monitors and controls function access to system resources; (4) Abnormal behavior detector: Identify potential malicious behavior or security threats; The workflow of the dynamic security policy executor is as follows: First, the code integrity verifier continuously verifies the integrity of the code during function loading and execution. It uses a lightweight hash verification algorithm that can provide strong security without significantly affecting performance.

[0127] Secondly, the runtime permission manager implements fine-grained permission control. It can dynamically adjust execution permissions based on the actual needs of the function and follow the principle of least privilege. This approach improves security without overly restricting the functionality of the function.

[0128] Then, the resource access monitor monitors the function's access to system resources in real time. It implements an intelligent quota management system that can dynamically adjust resource access restrictions based on the function's historical behavior and current context.

[0129] Finally, the Abnormal Behavior Detector uses machine learning algorithms to continuously analyze the behavior patterns of functions. It can promptly identify potential security threats, such as abnormal resource usage patterns or suspicious network activities, and trigger corresponding protective measures.

[0130] An important innovation of the security mechanism module is the "context-aware security policy". The system can dynamically adjust the strictness of the security policy based on factors such as the function's execution environment and data sensitivity, achieving a balance between security and performance.

[0131] The main innovations of this embodiment include: intelligent asynchronous scheduler to optimize asynchronous operation performance; adaptive concurrency control mechanism to improve resource utilization; dynamic security policy executor to achieve flexible and powerful security protection; context-aware security policy to balance security and performance.

[0132] These innovations significantly improve the performance and security of .NET Serverless functions. By optimizing asynchronous operations and implementing intelligent security mechanisms, the system not only improves the execution efficiency of functions, but also ensures safe operation in the Serverless environment.

[0133] It should be noted that the effects of asynchronous operation optimization and security mechanisms may vary depending on the specific characteristics of the function and the operating environment. For functions with fewer asynchronous operations or lower security requirements, the effects of these optimizations may not be as obvious as in other scenarios.

[0134] Example 8: API and integration solutions.

[0135] This example describes in detail the API design of the system and the integration solution with the mainstream .NET Serverless platform. This example demonstrates the practicality and scalability of the present invention, allowing developers to easily use and customize the optimization system and seamlessly integrate it into the existing Serverless environment.

[0136] The core of API design is a flexible and easy-to-use "optimization configuration interface". Fig.11 As shown in Figure 1, the interface includes an optimization strategy configurator, a performance monitoring interface, a diagnostic tool set, and an extension point system. Together, these components form a comprehensive API ecosystem that allows developers to control the optimization process in a fine-grained manner, monitor performance, diagnose problems, and extend system functionality.

[0137] The design of the optimization configuration interface follows the principles of simplicity, flexibility, scalability, and consistency. It provides high-level abstraction so that common optimization tasks can be completed through simple configuration, while allowing advanced users to fine-tune optimization behavior through detailed configuration. The interface usage process includes steps such as setting optimization strategies, obtaining performance indicators, generating optimization reports, and registering custom plug-ins.

[0138] The integration solution section describes how to integrate this optimization system into mainstream .NET Serverless platforms, such as Azure Functions. The core of the integration is an "adaptation layer" that coordinates the workflow of the optimization system and the Serverless platform. The adaptation layer manages the function lifecycle, coordinates resource usage, converts configuration formats, and integrates performance data.

[0139] An innovative feature of the integration process is the "non-intrusive deployment" mechanism. This mechanism integrates the optimization system into the Serverless environment through a lightweight "optimization agent" without modifying the existing function code and deployment process. The optimization agent inserts the necessary optimization logic at each stage of function execution to ensure that the optimization process minimizes the impact on the original system.

[0140] Another innovation is "cross-platform optimization strategy synchronization". This feature allows optimization strategies to be shared and synchronized between different Serverless platforms, allowing developers to maintain consistent optimization effects on multiple platforms. This is especially important for applications deployed across platforms, and can significantly reduce the complexity of configuration and management.

[0141] The main innovations of this embodiment are its flexible and easy-to-use API design, non-intrusive deployment mechanism, cross-platform optimization strategy synchronization, and an extension point system that supports the continued growth of the ecosystem. These innovations make this optimization system not only powerful, but also highly practical and scalable. Developers can easily integrate it into existing Serverless projects and customize and expand it as needed.

[0142] It should be noted that although the API and integration solutions are designed to be universal, additional adaptation work may be required on specific platforms or in special scenarios. In addition, some advanced optimization features may require platform support, so the performance on different platforms may vary.

[0143] This example is the last supplement to the entire .NET Serverless function optimization system, which demonstrates the actual application of the system and its integration with the existing technology ecosystem. By providing a flexible API and seamless integration solution, the present invention not only solves technical problems, but also takes into account the feasibility of actual implementation and promotion, providing strong support for the further development of Serverless computing. This comprehensive approach ensures that the optimization system can be used to its full potential in various .NET Serverless environments, while leaving ample room for future expansion and improvement.

[0144] It should be noted that the above embodiment is only one of the preferred implementations of the present invention, and those skilled in the art can adjust or optimize the specific implementation of each module according to actual needs. For example, the AOT compilation strategy can be adjusted according to the characteristics of different Serverless platforms, or the memory management strategy can be optimized for specific types of applications. In addition, the various modules of this system can also be used independently or integrated with other systems to meet different optimization needs. 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 .NET serverless functions, characterized in that: include: Dependency analysis module, which is used to perform static analysis and generate dependency graphs during the function deployment phase, and dynamically track the actually loaded assemblies and execution paths during runtime; AOT compilation optimization module, used to implement partial AOT compilation and optimize compilation for critical paths and hot code; Dynamic code optimization module, used to implement adaptive JIT compilation strategy and dynamically adjust compilation threshold according to function call pattern; Assembly loading optimization module, used to implement intelligent preloading system and lightweight assembly loader; Type system optimization module, used to implement type warm-up mechanism and generic specialization technology; Memory management optimization module, used to implement special memory allocation strategies for short-lifecycle functions; The state preservation and recovery module is used to implement lightweight runtime state serialization and fast recovery mechanism; Adaptive optimization system for continuous monitoring and dynamic adjustment of optimization strategies.

2. The system according to claim 1, characterized in that The AOT compilation optimization module also includes a cross-platform AOT compilation function for generating local code optimized for different serverless environments; the dynamic code optimization module also includes a code cache mechanism for saving the optimized local code for subsequent call reuse.

3. The system according to claim 1, characterized in that The assembly loading optimization module also includes an assembly cache mechanism for reducing the overhead of repeated loading and parsing; the type system optimization module also includes a value type layout optimization function for reducing boxing operations and memory allocation.

4. The system according to claim 1, characterized in that The memory management optimization module also includes an optimized garbage collection algorithm and an object pool mechanism for reducing GC pause time and reusing common object structures; the state preservation and recovery module also includes an incremental state update mechanism for saving and restoring only the changed parts.

5. The system according to claim 1, characterized in that It also includes an asynchronous operation optimization module, which is used to design efficient asynchronous state machines, optimize async / await mode, and implement a batch processing mechanism for asynchronous operations; And the security mechanism module is used to implement application domain level isolation, code access security enhancement mechanism and dynamic code audit system.

6. The system according to any one of claims 1 to 5, characterized in that: It also includes API and integration modules, which provide APIs that allow developers to customize optimization strategies and design integration solutions with mainstream .NET serverless platforms.

7. A method for optimizing the cold start time of a .NET serverless function, characterized in that: The following steps are involved: (1) Perform dependency analysis, including static analysis and dynamic tracing; (2) Perform AOT compilation optimization, including partial AOT compilation and cross-platform AOT compilation; (3) Implement dynamic code optimization, including adaptive JIT compilation and code caching; (4) Optimize assembly loading, including intelligent preloading and lightweight assembly loading; (5) Optimize the type system, including type preheating and generic specialization; (6) Optimize memory management, including special memory allocation strategies and garbage collection optimization; (7) Implement state preservation and fast recovery, including lightweight state serialization and incremental state updates; (8) Perform adaptive optimization, including continuous monitoring and dynamic adjustment of optimization strategies.

8. The method according to claim 7, characterized in that It also includes asynchronous operation optimization steps for optimizing the async / await pattern and implementing batch processing of asynchronous operations, as well as security assurance steps for implementing application domain-level isolation and dynamic code auditing.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 7 or 8 is implemented.

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