API Latency Estimation via Subcomponent Parameter Analysis
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Solution Overview
Problem
API back-ends are complex, making it difficult to predict execution latencies due to unknown paths through subcomponents, which hinders effective performance assessment and anomaly detection.
Innovation Solution
A system that learns and estimates latency based on parameters like fleet capacity, request volume, hardware type, and data characteristics, comparing estimated with actual latencies to identify anomalies and dynamically update the model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If latency measurements are collected for API service requests, then performance assessment capability is improved, but the complexity of analyzing execution latencies increases due to unknown paths through subcomponents
Solution Approach 1:
The patent segments the API back-end into multiple subcomponents (e.g., authentication service, data processing service, database service) and tracks execution paths through each segment. By breaking down the complex API into manageable subcomponents with known latency characteristics, the system can accurately measure and analyze overall latency without being overwhelmed by the complexity of the entire system.
Solution Approach 2:
The patent performs preliminary actions by pre-defining expected execution paths and latency ranges for each subcomponent before actual API requests are processed. The system establishes baseline latency expectations for authentication, data processing, and database operations in advance, enabling accurate anomaly detection when actual latencies deviate from these pre-established expectations.
2Adaptability or versatility
If the API back-end is made more complex to handle various workflows, then service versatility is improved, but latency prediction accuracy deteriorates due to multiple possible paths
Solution Approach 1:
The patent implements dynamic latency estimation by continuously monitoring actual execution paths and updating expected latency ranges for each subcomponent. The system adapts its latency predictions based on real-time performance data, allowing it to maintain accuracy even as the API back-end handles increasingly complex and varied workflows. This dynamic adjustment enables the system to accommodate workflow flexibility while preserving prediction reliability.
Solution Approach 2:
The patent employs feedback mechanisms where actual latency measurements from executed API requests are fed back into the system to refine and update the expected latency ranges for each subcomponent. This feedback loop allows the system to learn from actual execution patterns and improve its latency predictions over time, even as the API handles more diverse and complex workflows.
3Reliability
If execution latencies are monitored to detect anomalies, then system reliability is improved, but false alarms increase due to unknown latency variations
Solution Approach 1:
The patent changes the parameters used for anomaly detection from static threshold-based approaches to dynamic range-based approaches. Instead of using fixed latency thresholds that cause false alarms, the system calculates expected latency ranges for each subcomponent based on actual execution patterns and data characteristics. This parameter adaptation allows the system to distinguish between normal latency variations and genuine anomalies, significantly reducing false alarms while maintaining high reliability.
Data Source
AI summary
An API latency estimation system estimates latencies as a function of subcomponent parameters. The system may obtain first information indicative of at least a characteristic of data of a request provided to an API and second information indicative of at least a utilization of a first subcomponent of the API used to fulfill a subtask of a task of the request. An estimated latency for the first subcomponent to fulfill the subtask is determined at least in part by applying a latency estimation model for the API to at least the first information and the second information. If a comparison of the estimated latency to a measured latency for the first subcomponent to perform the subtask indicates a potential anomaly, then an indication of the potential anomaly may be outputted. The model may be updated with API request fulfillment data that is not anomalous.