Microservice Rate Limiting With Predictive Retry Scheduling
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Solution Overview
Problem
Conventional rate limiting and retry strategies for microservices are overly sensitive to extraneous factors, leading to inaccurate reflection of service endpoint throughput capacity, resulting in service endpoint overload, delayed recovery from transient failures, and failure to meet Quality of Service (QoS) standards and Service Level Agreements (SLAs).
Innovation Solution
A provider service maintains a request log to extract failure count and response duration data, uses a regression model to adjust rate limiting thresholds based on learned throughput capacity, and employs a time series forecasting model to predict future time slots for retrying delayed requests when the endpoint can handle the load, thereby applying time-varying and accurate rate limiting and retry strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional rate limiting strategies are applied to service endpoints, then request volume is controlled, but service endpoint throughput capacity is not accurately reflected leading to overload
Solution Approach 1:
The system implements feedback by continuously monitoring service endpoint responses and adjusting rate limiting thresholds dynamically. The provider service extracts failure counts and response durations from request logs, uses machine learning models to predict throughput capacity, and adapts rate limiting thresholds based on predicted capacity rather than using fixed thresholds. This closed-loop feedback mechanism ensures rate limiting accurately reflects actual service capacity.
Solution Approach 2:
The patent applies dynamics by transitioning from static rate limiting thresholds to dynamic, time-varying thresholds. The system uses time series forecasting models to predict future throughput capacity and adjusts rate limiting thresholds in real-time based on predicted capacity. This dynamic adaptation allows the rate limiting strategy to respond to changing service conditions and accurately reflect throughput capacity variations.
2Reliability
If requests are delayed due to rate limiting, then service endpoint overload is prevented, but retry timing is not optimized leading to delayed recovery
Solution Approach 1:
The system applies preliminary action by forecasting future throughput capacity before attempting retries. The provider service uses time series forecasting models to predict when service endpoint capacity will be sufficient to handle additional requests. Retry operations are scheduled in advance based on these predictions, ensuring requests are sent at optimal times when the service can handle them, thus preventing overload while minimizing delay.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically determine optimal retry timing without external intervention. The machine learning models continuously learn from service endpoint responses and autonomously predict capacity patterns. The system self-adjusts retry schedules based on learned patterns, eliminating the need for manual retry policy configuration and ensuring timely recovery while preventing overload.
3Ease of manufacture
If fixed rate limiting thresholds are used, then implementation is simple, but QoS standards and SLAs are not met due to inaccurate capacity reflection
Solution Approach 1:
The system applies parameter changes by transforming fixed rate limiting thresholds into dynamic, adaptive parameters. Instead of using constant threshold values, the system employs machine learning models to predict throughput capacity and continuously adjusts rate limiting thresholds based on predicted capacity parameters. This parameter transformation maintains implementation feasibility while significantly improving QoS and SLA compliance through accurate capacity reflection.
Data Source
AI summary
In accordance with the described techniques, a provider service receives a request log of a service endpoint including requests sent to the service endpoint by the provider service. The provider service extracts request log data from the request log including a failure count and an average response duration for the requests. The failure count includes the requests that have failed due to the provider service sending too many requests to the service endpoint and the requests that have failed due to server-side errors of the service endpoint. A throughput capacity for the service endpoint is predicted using a machine learning model based on the failure count and the average response duration. Then, the provider service adjusts a rate limiting threshold for the service endpoint based on the throughput capacity, and the rate limiting threshold defines a rate at which the provider service sends requests to the service endpoint.