Function Call Interceptor for Stateless Compute Performance Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generating and analyzing performance metrics for stateless compute functions is challenging due to the lack of internal state, making it difficult to monitor and determine sudden increases in execution times, which can lead to inefficiencies and degraded customer experiences in computing services.
Innovation Solution
Implementing a system to monitor stateless compute functions by tracking quasi-states such as before, during, and after function calls, and using agents to gather and analyze time-based metrics without directly instrumenting the functions, allowing for the collection of performance data without altering the stateless nature of the compute functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stateless compute functions are used to provide scalable computing services, then resource efficiency and scalability are improved, but performance monitoring and analysis become difficult due to lack of internal state
Solution Approach 1:
The patent introduces function call interceptors as intermediary components that sit between the stateless compute functions and the external service calls. These interceptors capture function invocation data, execution timing, and performance metrics without requiring any modification to the stateless functions themselves. The interceptors maintain a logging mechanism that records performance data in a structured format, enabling comprehensive monitoring while preserving the stateless nature of the compute functions.
2Measurement precision
If function call interceptors are implemented to monitor performance, then performance analysis capability is improved, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct modular components: function call interceptors that capture invocation data, performance metric collectors that gather execution statistics, data processors that analyze the collected metrics, and reporting mechanisms that present the results. Each component has a specific responsibility and can be independently configured, maintained, and scaled. This segmentation reduces overall system complexity by creating clear separation of concerns while enabling comprehensive performance monitoring.
Data Source
AI summary
Techniques for acquiring performance analysis metrics for computer-implemented functions are described herein. A computer-implemented function is instantiated on a computer system. A monitoring agent running on the computer system monitors outbound calls from the computer-implemented function and, when an outbound function call is intercepted, the agent calculates a performance metric for the outbound function. An aggregated set of performance metrics is produced, and that aggregated set of performance metrics is presented for further analysis.