Two-Tier Reinforcement Learning for Edge Node Overload Protection
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Solution Overview
Problem
Edge node overloading in edge computing clusters due to sudden influxes of client requests, leading to reduced resources, service failures, and poor user experience, is not effectively addressed by existing reactive overload protection mechanisms.
Innovation Solution
Implementing two-tier reinforcement learning models, one at the edge node level and one at the cluster level, to optimize routing and redirection policies, determining whether to handle or redirect client requests, thereby managing CPU load fluctuations and maintaining service availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge nodes handle all client requests directly, then service availability is maintained, but edge nodes become overloaded during traffic spikes
Solution Approach 1:
The patent introduces a load manager as an intermediary component that sits between client devices and edge nodes. This load manager receives client requests, evaluates current edge node capacity using reinforcement learning models, and intelligently routes requests to appropriate nodes. The intermediary prevents direct overload of edge nodes while maintaining service availability by acting as a traffic coordinator that balances load distribution across the edge computing infrastructure.
2Reliability
If reactive overload protection is used, then edge node failures are addressed, but service disruptions occur before protection activates
Solution Approach 1:
The patent implements preliminary action through proactive overload protection mechanisms that monitor edge node capacity metrics in real-time and predict potential overloads before they occur. The reinforcement learning models continuously learn from historical traffic patterns and current system state to anticipate capacity issues. When potential overload conditions are detected, the load manager proactively redistributes traffic before the edge node actually fails, preventing service disruptions rather than reacting to them after failure occurs.
Solution Approach 2:
The system employs continuous feedback loops where the load manager monitors edge node performance metrics (CPU utilization, memory usage, request processing rates) and feeds this information back to the reinforcement learning models. The models use this feedback to dynamically adjust routing decisions and predict future capacity needs. This closed-loop feedback mechanism enables the system to adapt to changing conditions and maintain optimal load distribution, preventing both underutilization and overload of edge nodes.
3Reliability
If edge nodes are deployed in clusters for high availability, then service continuity is improved, but system complexity increases
Solution Approach 1:
The patent merges the load management and routing functions into a single centralized load manager that handles multiple edge nodes through a unified interface. Instead of each edge node independently managing its own load or requiring complex peer-to-peer coordination, the consolidated load manager provides centralized control over traffic distribution across the entire edge cluster. This merging of functions simplifies cluster management while maintaining high availability, as the load manager can dynamically route requests across any available node in the cluster without requiring complex inter-node communication protocols.
Data Source
AI summary
An overload protection mechanism for network nodes in an edge node cluster uses two tier reinforcement learning models; one at the edge node level and one at the cluster level. The node level reinforcement learning model optimizes a routing policy for a service/application to determine whether an edge node receiving a client request shall handle the traffic for the service/application. The cluster level reinforcement learning model optimizes a policy for an application that determines which neighboring edge node shall be considered to handle a client request for the given application in case of redirection or proxy by the edge node receiving the client request.