Service Relocation for Edge Host Optimization
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Solution Overview
Problem
The complexity of managing and optimizing distributed systems with service-oriented architecture has increased due to scale and scope, leading to challenges in provisioning, administration, and performance optimization, particularly in network-based marketplaces that rely on multiple interconnected services.
Innovation Solution
A system and method for optimizing service-oriented systems using trace data to monitor interactions, generate performance metrics, and create optimized configurations by relocating services, modifying service instances, and applying static analysis to improve latency, throughput, and cost efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If services are distributed across multiple hosts in a service-oriented architecture, then system scalability and reliability are improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent introduces an optimization system as an intermediary that acts as a mediator between service consumers and distributed service providers. This optimization system collects trace data from multiple services across different hosts, analyzes performance metrics, and automatically generates optimized service configurations. By centralizing the optimization and management functions in this intermediary system, the complexity of managing distributed services is reduced while maintaining the reliability benefits of service distribution.
Solution Approach 2:
The optimization system dynamically changes service configuration parameters based on analyzed trace data and performance metrics. It generates optimized service configurations that modify parameters such as service locations, hosting arrangements, and interaction patterns. This automatic parameter adjustment allows the system to maintain optimal performance and reliability across distributed services without requiring manual intervention, thereby reducing management complexity.
2Loss of time
If services are relocated to optimize performance, then network latency is reduced, but system complexity and reconfiguration difficulty increase
Solution Approach 1:
The optimization system performs preliminary analysis of trace data and performance metrics before executing service relocations. It proactively identifies optimization opportunities and prepares optimized service configurations in advance. By analyzing historical trace data and predicting future performance improvements, the system can plan and execute service relocations systematically, reducing the complexity of reconfiguration while achieving latency reduction goals.
Solution Approach 2:
The system continuously collects trace data from service interactions and uses this feedback to automatically adjust service configurations. The feedback loop enables the optimization system to monitor performance metrics, identify latency issues, and automatically relocate services to optimize performance. This closed-loop feedback mechanism simplifies the reconfiguration process by making it automatic and data-driven rather than manual and ad-hoc.
3Measurement precision
If comprehensive trace data is collected from all services, then performance analysis accuracy is improved, but data processing complexity and resource consumption increase
Solution Approach 1:
The optimization system extracts and focuses on collecting only the most relevant trace data and performance metrics needed for effective service optimization. Rather than indiscriminately collecting all possible data from every service, it identifies and extracts key metrics such as service response times, interaction frequencies, and critical performance indicators. This selective extraction maintains high analysis accuracy while reducing data processing complexity and resource consumption.
Data Source
AI summary
Methods, systems, and computer-readable media for implementing service-oriented system optimization using edge 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 edge hosts based on the optimized configuration. The relocation improves a total performance metric in at least a portion of the service-oriented system.