Adaptive API Call Sequence Detection for Production Simulation
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Solution Overview
Problem
Existing production systems lack accurate simulation methods for API call sequences, leading to non-optimal configuration and inefficient resource utilization, resulting in increased client latency and resource wastage.
Innovation Solution
Adaptive API call sequence detection generates precise API call sequences based on response and gap times to simulate the production environment accurately, allowing for optimal reconfiguration and improved resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simulation methods are used for API call sequences, then the production system can be tested, but the simulation results are inaccurate leading to non-optimal configuration
Solution Approach 1:
The system performs preliminary detection of API call sequences, response times, and gap times before simulation to establish accurate baseline data. This preliminary measurement enables the simulation to reflect real production conditions more accurately, resolving the contradiction between simulation accuracy and resource utilization efficiency.
Solution Approach 2:
The system continuously monitors production system performance, compares simulated results with actual performance metrics, and adjusts the simulation model accordingly. This feedback mechanism ensures that simulation accuracy improves over time while maintaining optimal resource utilization through data-driven configuration adjustments.
2Ease of manufacture
If the production system is reconfigured without accurate simulation data, then configuration changes can be made quickly, but resource wastage increases due to non-optimal configuration
Solution Approach 1:
Accurate API call sequence detection and simulation are performed before reconfiguration to establish optimal configuration parameters in advance. This preliminary analysis ensures that when reconfiguration is executed, it is based on proven optimal settings rather than trial-and-error, thus maintaining both speed and efficiency.
Solution Approach 2:
The system creates accurate copies of production API call sequences and gap times from the live environment and uses these copies for simulation and testing. This allows configuration optimization to be performed on replicated data without affecting actual production operations, enabling rapid iteration and optimal configuration discovery without resource wastage.
3Measurement precision
If detailed API call tracking is implemented, then simulation accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts only the critical elements needed for accurate simulation - specifically API call sequences, response times, and gap times - from the complex production environment. By focusing on these essential parameters rather than tracking every system detail, the detection system achieves high accuracy while maintaining manageable complexity.
Solution Approach 2:
The detection system is designed to handle multiple types of API calls, response time measurements, and gap time calculations using a unified approach. This multi-functional design reduces overall system complexity by consolidating detection, measurement, and analysis functions into a single integrated system rather than requiring separate mechanisms for each function.
Data Source
AI summary
One or more computing devices, systems, and/or methods for adaptive API call sequence detection are provided. A series of API calls and gap times between API calls of the series of API calls are recorded. The API calls are received and processed by a production system. The API calls are assigned into API call sequences. An end of an API call sequence is detected based upon a minimum response time and the gap times between the API calls. The API call sequences are utilized to simulate execution of the production system. A configuration is generated and applied to the production system based upon a result of the simulation.