Partial Service Relocation for Latency 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
1Adaptability or versatility
If services are distributed across multiple hosts in a service-oriented architecture, then system scalability and flexibility are improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent segments the service portfolio into distinct service categories (e.g., compute services, storage services, database services) and deploys them across different hosts in an organized manner. This segmentation allows the system to maintain flexibility through distributed architecture while reducing complexity by creating clear service boundaries and deployment patterns that are easier to manage.
2Adaptability or versatility
If more services are deployed to handle increased market demand, then system functionality and service coverage are improved, but resource consumption and operational costs increase
Solution Approach 1:
The patent implements universal service templates and standardized service configurations that can be replicated across multiple hosts. Instead of deploying unique services for each demand scenario, the system uses multi-functional service templates that can adapt to different market requirements, thereby expanding service coverage without proportionally increasing resource consumption through duplication.
Solution Approach 2:
The system dynamically adjusts service deployment parameters such as instance count, resource allocation, and service configuration based on real-time market demand analysis. By changing these parameters rather than always deploying maximum services, the system achieves comprehensive service coverage while optimizing resource utilization and reducing unnecessary resource consumption.
3Loss of time
If service interactions are monitored and optimized using trace data, then system performance and latency are improved, but data processing requirements and analysis complexity increase
Solution Approach 1:
The patent implements self-service monitoring and optimization mechanisms where services automatically generate, collect, and analyze their own trace data. The system uses automated performance analysis tools that continuously monitor service interactions and self-optimize deployment configurations without requiring complex external analysis infrastructure, thereby reducing latency while keeping analysis complexity manageable through automation.
4Productivity
If services are relocated to optimize performance, then latency and throughput are improved, but provisioning complexity and administrative overhead increase
Solution Approach 1:
The patent implements dynamic service relocation capabilities where services can be automatically moved between hosts based on real-time performance metrics and load conditions. This dynamic approach optimizes throughput by placing services on appropriate hosts while reducing provisioning complexity through automated decision-making algorithms that eliminate manual administrative overhead for service placement decisions.
Solution Approach 2:
The system uses feedback loops that continuously monitor service performance, latency, and resource utilization, then automatically adjust service locations based on this feedback. This closed-loop control optimizes throughput by relocating services to improve performance while simplifying provisioning operations through automated feedback-driven decisions rather than manual administrative actions.
Data Source
AI summary
Methods, systems, and computer-readable media for implementing service-oriented system optimization using partial service relocation are disclosed. An optimized configuration is determined for the service-oriented system based on performance data. The optimized configuration improves a performance metric in the service-oriented system. A partial service is automatically generated based on an original service in a service-oriented system. The partial service includes a first set of program code from the original service and excludes a second set of program code from the original service. The first set of program code is included in the partial service based on its frequency of use. One or more instances of the partial service are deployed to the service-oriented system based on the optimized configuration.