Service Graphs for Microservice Version Comparison
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Solution Overview
Problem
Existing systems lack an efficient method to compare and transition between different versions of microservices during deployment, particularly in canary deployments, which can impact network traffic, latency, and error rates, making it difficult to determine the performance of new versions and smoothly phase out old ones.
Innovation Solution
The use of service graphs to monitor and compare metrics such as traffic volume, latency, and error rates between different versions of microservices, allowing for gradual traffic diversion from old to new versions based on performance differences, enabling controlled deployment and phasing out of old versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple versions of microservices are deployed concurrently for canary deployment, then the ability to compare performance and gradually phase out old versions is improved, but the complexity of monitoring and managing multiple versions increases
Solution Approach 1:
The system segments microservice versions into distinct deployable units (canary versions vs. stable versions) that can be independently monitored and managed. Each version is tracked separately through version identifiers in the service graph, allowing granular control over deployment phases without managing all versions as a single complex system
Solution Approach 2:
The service graph continuously monitors performance metrics (latency, error rates, traffic volume) for each microservice version and provides feedback to the deployment system. This feedback mechanism enables automatic comparison between versions and triggers phased traffic redistribution based on observed performance differences, reducing manual monitoring complexity
2Reliability
If network traffic is gradually diverted from old to new microservice versions, then seamless transitions with minimal disruptions are achieved, but the time required for complete deployment increases
Solution Approach 1:
The system dynamically adjusts traffic distribution ratios between microservice versions based on real-time performance monitoring. The service graph enables flexible, adaptive traffic routing that can accelerate or decelerate the transition pace according to observed system behavior, optimizing both transition smoothness and deployment speed rather than following a fixed timeline
Solution Approach 2:
The deployment system changes traffic volume parameters gradually in controlled increments, shifting the proportion of network traffic directed to new versus old microservice versions. This parameter-based control allows precise management of transition speed and smoothness, balancing deployment time against transition reliability
3Manufacturing precision
If performance metrics are closely monitored to determine deployment success, then deployment quality is improved, but the complexity of metric collection and analysis increases
Solution Approach 1:
The service graph performs multiple functions simultaneously: it monitors performance metrics, compares versions, determines deployment success, and triggers traffic redistribution. This multi-functional approach consolidates what would otherwise be separate complex systems into a single unified platform, reducing overall system complexity while maintaining high deployment quality
Solution Approach 2:
The system automatically collects, analyzes, and interprets performance metrics without requiring external manual analysis. The service graph self-services by comparing metrics between versions, determining whether deployment goals are met, and autonomously initiating traffic redistribution decisions, eliminating the need for complex manual metric analysis processes
Data Source
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AI summary
Described embodiments provide systems and methods for using service graphs to compare performance of a plurality of versions of a microservice. A device may establish metrics from execution of a plurality of versions of a microservice of a service. The plurality of versions of the microservice are deployed concurrently for a portion of execution of the service. The device generates service graphs for each version of the plurality of versions of the microservice. The service graphs include metrics from monitoring execution of a respective version of the microservice. The device identifies differences in metrics between the service graphs for different versions of the microservice. The device requests a change in network traffic of the service between respective versions of the microservice based at least on the one or more differences.