Behavior Profile Error Detection for Web Service API Reliability
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Solution Overview
Problem
Complex systems in large companies face increased errors due to interactions among multiple systems, leading to reduced efficiency, particularly in web services that handle API requests from various entities.
Innovation Solution
The system detects errors by creating and comparing behavior profiles based on user requests, logging and grouping API calls by parameters, and flagging discrepancies in error rates to identify and address issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple systems interact to communicate in large companies, then functionality and versatility are improved, but error rates increase and efficiency decreases
Solution Approach 1:
The system performs preliminary actions by establishing baseline behavior profiles for API calls before errors occur. These profiles capture normal error rates and response patterns, enabling the system to detect deviations from expected behavior and identify anomalies proactively rather than reactively.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual API call behavior against established behavior profiles. When deviations are detected, the system provides feedback signals that trigger investigations into potential issues, allowing for real-time error detection and response in complex multi-system interactions.
2Adaptability or versatility
If web services handle requests from multiple entities, then service versatility is improved, but error detection difficulty increases
Solution Approach 1:
The system segments the monitoring process by creating separate behavior profiles for different API calls, entities, and time periods. This segmentation allows the system to track and compare specific subsets of requests against their respective baselines, making error detection more manageable despite the complexity of multi-entity interactions.
Solution Approach 2:
The system dynamically adapts its monitoring approach by adjusting behavior profiles based on changing patterns in API usage. As the system learns from new data, it updates profiles to reflect current normal behavior, enabling it to detect errors in evolving multi-entity request patterns without manual reconfiguration.
3Adaptability or versatility
If system complexity increases to handle more interactions, then functionality is improved, but efficiency decreases
Solution Approach 1:
The system performs self-service by automatically generating, maintaining, and updating behavior profiles without requiring manual intervention. It autonomously monitors API calls, compares them against profiles, and identifies anomalies, reducing the need for human resources and improving efficiency despite increased system complexity.
Solution Approach 2:
The system manages complexity by transforming it into measurable parameters within behavior profiles, such as error rates, response times, and request patterns. By quantifying these parameters and establishing baselines, the system can efficiently monitor and detect deviations without being overwhelmed by the underlying complexity of multi-system interactions.
Data Source
AI summary
Methods, systems, and computer-readable media for detecting errors within a system by using behavior profiles are presented. At a first time, user requests may be received and serviced. The serviced user requests may be logged. Based on the logged user requests, profiles may be determined. At a second time, user requests may be received and serviced. The serviced user requests may be logged. The logged serviced user requests may be compared to the profiles determined at a first time. For example, the determined profiles may include an error rate for serviced user requests. At the second time, an error rate for the logged serviced user requests may be compared to an error rate included the determined profiles. Serviced users requests may be flagged based on the comparison.