Edge Computing Recommendation System for Latency Optimization
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Solution Overview
Problem
Current edge computing models face challenges in efficiently recommending and implementing edge computing services that meet specific latency and resource requirements, as they often lack the necessary infrastructure at geographically optimal locations, leading to suboptimal performance and increased costs.
Innovation Solution
A system and method that allow users to specify latency and resource requirements, recommending and automatically procuring necessary resources from geographically optimal edge computing sites, which may involve determining available resources, adding missing resources, and estimating costs, to provide computing services with minimal latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge computing services are provided from geographically distant provider sites, then resource availability may be sufficient, but latency increases and performance deteriorates
Solution Approach 1:
The patent implements local quality by deploying computing resources specifically at edge locations geographically close to customers. The system identifies and provisions resources at edge sites that are optimally positioned to serve specific customer regions, ensuring that service delivery occurs locally rather than from distant centralized data centers. This resolves the contradiction by making service availability local while maintaining low latency through geographic proximity.
Solution Approach 2:
The patent segments the computing infrastructure into multiple distributed edge locations rather than relying on a single centralized site. By dividing the service delivery function across multiple geographically distributed edge sites, the system can provide local service availability while minimizing latency through intelligent site selection and resource placement at each segment.
2Loss of time
If computing resources are pre-deployed to all potential edge locations, then service latency is minimized, but infrastructure cost and complexity increase
Solution Approach 1:
The patent implements dynamics by making the edge resource deployment adaptive and on-demand rather than static and predetermined. The system dynamically identifies which edge locations require resources based on actual customer requests and service patterns, provisioning resources only where and when needed. This resolves the contradiction by maintaining low latency through rapid resource deployment to relevant locations without the burden of pre-deploying resources to all potential sites.
Solution Approach 2:
The system employs self-service mechanisms where edge resources are automatically provisioned and configured based on detected service requirements. Rather than requiring manual planning and deployment to all locations, the system autonomously identifies optimal edge sites and deploys necessary resources, reducing infrastructure complexity while maintaining the capability to minimize latency when needed.
3Productivity
If comprehensive resource provisioning is performed at edge sites, then service performance is optimized, but procurement complexity and cost increase
Solution Approach 1:
The patent implements feedback by continuously monitoring service performance metrics and resource utilization at edge locations. This feedback loop enables the system to identify when and where additional resources are needed to optimize performance, triggering targeted procurement actions only for specific locations and resource types. This resolves the contradiction by optimizing service performance through data-driven resource allocation rather than comprehensive provisioning everywhere, thereby reducing procurement complexity.
Solution Approach 2:
The system performs preliminary actions by pre-establishing relationships with resource providers and preparing procurement processes in advance. When performance optimization is needed at a specific edge site, the system can rapidly provision resources because the procurement framework is already in place. This resolves the contradiction by enabling optimized performance through pre-prepared procurement capabilities rather than complex ad-hoc provisioning, reducing overall procurement complexity.
Data Source
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AI summary
In examples, systems and methods are described for edge computing recommendations and implementation. A service request is received from a client computing device that includes information about a location of a customer site and latency requirements of the customer, among other information. The system provides recommendations for particular provider computing site(s) based on, e.g., rough and/or fine latency estimates, and implements the requested computing services at selected provider computing site(s).