Administrator-Monitored RL Application Manager for Distributed Systems
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Solution Overview
Problem
The increasing complexity of distributed computing systems has led to management and administration inefficiencies, with traditional approaches becoming impractical due to high computational overheads and complexity, necessitating alternative methodologies such as machine-learning-based solutions for effective optimization and cost reduction.
Innovation Solution
An administrator-monitored reinforcement-learning-based application manager is deployed to manage computational environments with reward-specified goals, allowing human oversight and intervention to constrain exploration and ensure safe control actions, thereby optimizing system performance while avoiding detrimental actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional management approaches are used for distributed computing systems, then system control is straightforward, but computational overhead and complexity increase significantly
Solution Approach 1:
The system employs reinforcement learning agents that autonomously learn and optimize management policies for distributed computing resources. The agents self-adjust resource allocation, scheduling, and configuration without requiring manual intervention or complex centralized control mechanisms, thereby reducing both management complexity and computational overhead through adaptive self-optimization.
2Productivity
If reinforcement learning is used to manage computational environments, then optimization performance improves, but exploration of state/action space may lead to detrimental actions
Solution Approach 1:
The system implements human-in-the-loop feedback mechanisms where administrators review and approve reinforcement learning agent decisions before execution. The feedback loop includes monitoring exploration actions, evaluating their impact on system state, and adjusting exploration parameters dynamically. This ensures optimization performance while preventing detrimental actions through continuous human oversight and adaptive constraint adjustment.
3Reliability
If human oversight is added to monitor reinforcement learning actions, then safety and reliability improve, but administrative overhead increases
Solution Approach 1:
The system implements selective human oversight where administrators are only engaged for reviewing and approving significant or high-risk actions proposed by the reinforcement learning agent. Routine or low-impact decisions are executed automatically without human intervention. This partial oversight approach maintains system reliability for critical operations while minimizing administrative overhead by avoiding unnecessary human involvement in routine matters.
Data Source
AI summary
The current document is directed to an administrator-monitored reinforcement-learning-based application manager that can be deployed in various different computational environments to manage the computational environments with respect to one or more reward-specified goals. Certain control actions undertaken by the administrator-monitored reinforcement-learning-based application manager are first proposed, to one or more administrators or other users, who can accept or reject the proposed control actions prior to their execution. The reinforcement-learning-based application manager can therefore continue to explore the state/action space, but the exploration can be parametrically constrained as well as by human-administrator oversight and intervention.