Service Relocation for Latency Reduction in Distributed Systems
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Solution Overview
Problem
The complexity of managing and optimizing distributed systems with service-oriented architecture has increased due to their scale and scope, leading to challenges in provisioning, administration, and performance optimization, particularly in reducing latency and costs.
Innovation Solution
The implementation of a system that monitors service interactions, generates trace data, and uses performance metrics and call graphs to optimize service configurations by relocating services, modifying service instances, and optimizing request routing, thereby improving performance and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If services are distributed across multiple hosts in a service-oriented architecture, then system scalability and resource utilization are improved, but system complexity and management difficulty increase
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring service interactions and generating trace data that flows back to the optimization system. This feedback loop enables automatic analysis of service performance and configuration optimization without manual intervention, resolving the contradiction by allowing distributed scaling while maintaining manageable complexity through automated control.
Solution Approach 2:
The optimization system performs self-service by automatically analyzing trace data, generating optimized service configurations, and relocating services without requiring manual management. This self-service capability enables the system to handle its own complexity while supporting distributed architecture scalability.
2Productivity
If service interactions are monitored and trace data is generated for optimization, then service configuration optimization is improved, but data processing overhead and system resource consumption increase
Solution Approach 1:
The patent applies partial action by monitoring and analyzing only the necessary service interactions and trace data required for optimization, rather than processing all possible data. This selective approach enables effective service configuration optimization while minimizing unnecessary data processing overhead and resource consumption.
3Loss of time
If services are relocated based on optimized configurations, then service latency is reduced, but relocation overhead and service disruption increase
Solution Approach 1:
The patent implements preliminary action by generating optimized service configurations in advance based on trace data analysis, before actual service relocation occurs. This advance preparation allows services to be relocated with minimal disruption, as the optimization work is completed beforehand and services can transition smoothly to new configurations.
4Adaptability or versatility
If the scope and scale of distributed systems are increased, then system capability and service coverage are improved, but provisioning and management complexity increase
Solution Approach 1:
The patent enables distributed systems to self-manage their complexity through automated optimization. The system automatically analyzes service interactions, generates optimized configurations, and performs service relocation without manual provisioning, allowing the system to scale in capability and coverage while maintaining manageable complexity through self-service automation.
Data Source
AI summary
Methods, systems, and computer-readable media for implementing service-oriented system optimization using client device relocation are disclosed. An optimized configuration is determined for a service-oriented system based on trace data for a plurality of service interactions between services. One or more of the services are relocated to one or more client devices based on the optimized configuration. The relocation improves a total performance metric in at least a portion of the service-oriented system.