Local Agents for Distributed Application Management
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Solution Overview
Problem
The increasing complexity of distributed computing systems has led to management and administration challenges, including significant inefficiencies and computational overheads, making traditional automated management systems impractical, and there is a need for more effective optimization and cost reduction in managing these systems.
Innovation Solution
An automated reinforcement-learning-based application manager using local agents provides finer-granularity monitoring and continued management by decomposing actions and state information, allowing for efficient control and redundancy in case of network interruptions, enabling optimal policy learning and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automated management systems are used for distributed computing systems, then system management is provided, but significant inefficiencies and computational overheads occur
Solution Approach 1:
The system segments the centralized management function into multiple distributed local agents that operate autonomously at different levels of the computing hierarchy. Each agent manages its local components independently, eliminating the computational overhead of centralized control while maintaining coordination through peer-to-peer communication.
Solution Approach 2:
Local agents are equipped with autonomous decision-making capabilities using reinforcement learning, enabling them to self-manage their respective computing resources without requiring constant centralized intervention. This self-service approach significantly reduces computational overhead while improving management efficiency.
2Reliability
If centralized application manager is used, then application management is provided, but network interruption causes management disruption
Solution Approach 1:
The centralized manager is segmented into multiple local agents distributed across the computing system. Each agent maintains local state and can continue operation independently during network interruptions, ensuring management continuity without requiring complex centralized coordination infrastructure.
Solution Approach 2:
Each local agent possesses localized management capabilities tailored to its specific computing environment, allowing it to continue autonomous operation during network disruptions. This local autonomy improves reliability without significantly increasing overall system complexity.
3Measurement precision
If fine-granularity monitoring is implemented, then monitoring precision is improved, but system complexity increases
Solution Approach 1:
Fine-granularity monitoring is achieved by distributing monitoring functions across multiple local agents, each responsible for its own computational components. This segmentation enables detailed local monitoring without centralizing the complexity of managing all monitoring data, as each agent independently tracks and reports only its local state.
Data Source
AI summary
The current document is directed to automated reinforcement-learning-based application managers that use local agents. Local agents provide finer-granularity monitoring of an application or application subcomponents and provide continued application management in the event of interruption of network traffic between an automated reinforcement-learning-based application manager and the application or application subcomponents managed by the automated reinforcement-learning-based application manager.