Reinforcement Learning Application Manager Model Reuse
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Solution Overview
Problem
The increasing complexity of distributed computing systems has led to significant inefficiencies and computational overheads in management and administration, making traditional approaches impractical for scalable and cost-effective management.
Innovation Solution
An automated reinforcement-learning-based application manager that reuses learned models and leverages human-management experience to truncate state and observation vectors, optimizing computational efficiency by partially or completely reusing learned models and utilizing a subset of metrics for control policy learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional management approaches are used for distributed computing systems, then system management functionality is provided, but computational overheads increase and efficiency decreases
Solution Approach 1:
The system performs preliminary learning of management policies using reinforcement learning algorithms before actual system operation. The learned policies are stored and reused for similar system states, avoiding the need to compute optimal actions from scratch during runtime. This preliminary action phase separates the computationally intensive learning process from the operational phase, reducing ongoing computational overhead while maintaining high management efficiency
Solution Approach 2:
The system creates and stores copies of learned management policies and models for reuse. When the system encounters a state similar to previously learned states, it copies and applies the corresponding learned policy rather than recalculating. This copying mechanism significantly reduces computational overhead during operation while maintaining productivity, as the heavy lifting of policy optimization is done once during the learning phase
2Reliability
If complete state vectors are used for reinforcement learning, then accurate control policies are learned, but computational complexity increases
Solution Approach 1:
The system extracts and removes irrelevant or redundant features from the complete state vector, keeping only the most informative subset of state variables for reinforcement learning. This extraction process reduces the dimensionality of the state space, thereby lowering computational complexity while preserving the essential information needed to learn accurate control policies. The selection of relevant features is based on their contribution to predicting system outcomes and guiding optimal actions
Solution Approach 2:
The system applies different levels of state representation to different situations or system components. Rather than using a uniform complete state vector everywhere, it selectively uses truncated or full state representations based on the specific context, system component, or operational phase. This local quality approach optimizes the balance between accuracy and computational complexity by applying higher fidelity representations only where necessary
3Extent of automation
If reinforcement learning is applied to manage distributed systems, then automated optimization is achieved, but development costs and time increase
Solution Approach 1:
The system performs preliminary learning and model training phases before deployment to production environments. During this preliminary action phase, the reinforcement learning algorithms are trained offline using historical data or simulated environments. Once trained, the learned models are deployed for automated management with minimal real-time computation. This separation of offline training and online execution significantly reduces development time and allows the automated system to be deployed more quickly
Solution Approach 2:
The system develops universal learned models and policies that can be applied across multiple similar distributed systems or workloads. Instead of training separate reinforcement learning models for each individual system, the learned policies are designed to be transferable and reusable across different instances. This universality reduces overall development time and costs by amortizing the learning process across multiple applications, while still achieving automated optimization for each specific system
Data Source
AI summary
The current document is directed to automated reinforcement-learning-based application managers that obtain increased computational efficiency by reusing learned models and by using human-management experience to truncate state and observation vectors. Learned models of managed environments that receive component-associated inputs can be partially or completely reused for similar environments. Human managers and administrators generally use only a subset of the available metrics in managing an application, and that subset can be used as an initial subset of metrics for learning an optimal or near-optimal control policy by an automated reinforcement-learning-based application manager.