Edge Function-Guided AI Request Routing in Hybrid Clouds
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Solution Overview
Problem
Cloud environments face challenges in efficiently handling high volumes of AI processing requests due to resource scarcity and complexity, leading to potential system overload, component failure, and reduced performance, especially in hybrid cloud settings where resource contention and big data processing are prevalent.
Innovation Solution
The method employs a machine learning model to determine hydration levels of cloud endpoints in a hybrid cloud environment, dynamically deploys edge functions on edge components to alternate between varying flow paths for routing AI processing requests, optimizing resource usage and minimizing edge case emergence by selecting routes based on endpoint capacity and performance trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI processing requests are routed to cloud endpoints in a hybrid cloud environment, then the system can handle large volumes of requests with flexibility, but resource contention and system overload occur leading to component failure and reduced performance
Solution Approach 1:
The patent implements dynamic routing that adjusts flow paths based on real-time hydration levels of cloud endpoints. The system continuously monitors resource availability and dynamically redirects requests between alternative flow paths, transforming the static routing infrastructure into a dynamic system that adapts to changing load conditions, thereby maintaining reliability while handling high request volumes
Solution Approach 2:
The patent introduces edge functions as intermediary components between request sources and cloud endpoints. These edge functions act as mediators that evaluate hydration levels and make intelligent routing decisions, preventing direct overload of cloud endpoints while maintaining system flexibility and request handling capacity
2Adaptability or versatility
If cloud endpoints are used to provide AI processing, then scalability and flexibility are improved, but resource scarcity and complexity increase leading to inefficient request handling
Solution Approach 1:
The patent implements self-service mechanisms where the routing system automatically monitors hydration levels and makes routing decisions without manual intervention. The system serves itself by dynamically adjusting flow paths based on real-time resource availability, reducing the operational complexity of managing scalable cloud infrastructure while maintaining adaptability
3Productivity
If traditional routing is used for AI processing requests, then system simplicity is maintained, but latency increases and performance deteriorates under high load
Solution Approach 1:
The patent implements feedback mechanisms where hydration levels of cloud endpoints are continuously monitored and used to adjust routing decisions in real-time. This closed-loop control system provides feedback about resource availability, enabling the routing mechanism to optimize flow paths dynamically and reduce latency under high load conditions while maintaining simple operation
Data Source
AI summary
Edge function-guided artificial intelligence (AI) request routing is provided by applying a machine learning model to predictors of cloud endpoint hydration to determine hydration levels of cloud endpoints, of a hybrid cloud environment, that provide AI processing, determining, for each edge component of a plurality of edge components of the hybrid cloud environment and each cloud endpoint of the cloud endpoints, alternative flow paths between the edge component and the cloud endpoint, the alternative flow paths being differing routes for routing data between the edge component and the cloud endpoint, and the alternative flow paths being of varying flow rates determined based on the hydration levels of the cloud endpoints, and dynamically deploying edge functions on edge component(s), the edge functions configuring the edge component(s) to alternate among the alternative flow paths available in routing AI processing requests from the edge component(s) to target cloud endpoints of the cloud endpoints.