Microservice Timeout and Retry Optimization via Constrained Black Box Search
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Solution Overview
Problem
Microservices-based systems face challenges in optimizing latency and error rates due to varying communication parameters like timeouts and retries, which affect the overall performance and reliability of cloud computing applications.
Innovation Solution
The method involves constrained black box optimization to select optimized values for timeout and retry parameters across microservices, ensuring end-to-end latency constraints are met while minimizing error rates, using techniques like Gibbs sampling and Bayesian search algorithms to efficiently tune communication parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If timeout and retry parameters are increased to improve reliability, then error rate decreases, but latency increases
Solution Approach 1:
The patent applies parameter changes by systematically varying timeout and retry parameters across different microservice configurations to find optimal values that balance reliability and latency. The constrained black box optimization explores the parameter space to identify configurations where error rates are minimized while latency remains within acceptable bounds.
Solution Approach 2:
The patent implements dynamics by making timeout and retry parameters adaptive rather than static. The system dynamically adjusts these parameters based on observed performance metrics and constraints, allowing the microservices to respond to changing conditions while maintaining optimal latency and error rate trade-offs.
2Loss of time
If timeout and retry parameters are optimized to reduce latency, then response time improves, but error rate increases
Solution Approach 1:
The patent uses parameter changes to systematically adjust timeout and retry values, exploring the trade-off space between latency and error rate. By changing these parameters in controlled ways and evaluating performance outcomes, the system identifies optimal configurations that achieve low latency while maintaining acceptable error rates within specified constraints.
Solution Approach 2:
The patent applies preliminary action by pre-configuring optimized timeout and retry parameters before the microservices go into production. The constrained black box optimization performs the parameter tuning in advance, allowing the system to start with pre-optimized settings that balance latency and error rate, rather than adjusting parameters reactively after deployment.
3Productivity
If manual tuning of communication parameters is performed, then system performance can be optimized, but complexity of operation increases
Solution Approach 1:
The patent implements self-service by enabling the microservices system to automatically tune its own communication parameters through constrained black box optimization. The system autonomously explores parameter spaces, evaluates performance metrics, and identifies optimal timeout and retry configurations without requiring manual intervention, thereby maintaining high performance while reducing operational complexity.
Solution Approach 2:
The patent applies feedback by using observed performance metrics (latency, error rates) to guide the parameter optimization process. The constrained black box optimization continuously monitors system behavior and uses this feedback to refine timeout and retry parameter selections, creating a closed-loop system that self-adjusts to maintain optimal performance.
Data Source
AI summary
A specification of a topology of a microservices application is obtained as a plurality of nodes corresponding to a plurality of microservices of the microservices application. The plurality of nodes includes a root node, each of the plurality of nodes other than the root node has a timeout parameter and a retries parameter. Via constrained black box optimization, optimized values are selected for the timeout parameter and the retries parameter for each of the plurality of nodes other than the root node, subject to satisfying a specified end-to-end latency for the microservices application and minimizing an error rate for the microservices application. The microservices application is configured in accordance with the optimized values. At least one external request to the root node is responded to with the microservices application configured in accordance with the optimized values.