Safe-Constrained 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 challenges, including significant inefficiencies and computational overheads, making traditional automated management and administration systems impractical, prompting the need for alternative methodologies such as machine-learning-based approaches.
Innovation Solution
A safe-operation-constrained reinforcement-learning-based application manager is deployed across various computational environments to manage systems with reward-specified goals, using stored action filters to constrain state/action-space exploration to safe actions, preventing deleterious impacts on the managed environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automated management and administration systems are used for distributed computing systems, then the systems can perform basic management functions, but they incur significant computational overheads and efficiency losses
Solution Approach 1:
The patent replaces traditional rule-based and heuristic management systems with a reinforcement learning-based automated management system. The RL agent learns optimal management policies through interaction with the distributed computing environment, substituting manual configuration and traditional automated management approaches with intelligent agents that adaptively optimize resource allocation, task scheduling, and system configuration based on real-time conditions
Solution Approach 2:
The patent dynamically adjusts management parameters and system configurations based on learned policies. The RL agent continuously learns and adapts management strategies by changing operational parameters such as resource allocation ratios, task scheduling intervals, and system configuration settings to optimize performance while minimizing computational overhead
2Productivity
If reinforcement learning is applied to manage complex computational environments, then optimization capability is improved, but the exploration of state/action space may cause harmful effects on the managed system
Solution Approach 1:
The patent introduces a safety filter as an intermediary component between the reinforcement learning agent and the managed computational environment. This filter acts as a mediator that intercepts actions proposed by the RL agent, validates them against safety constraints and system state conditions, and either approves or modifies them before execution. The filter prevents harmful actions while allowing beneficial optimizations to proceed
Solution Approach 2:
The patent implements preliminary safety checks and constraints before actions are executed in the managed environment. The safety filter proactively identifies and blocks potentially harmful actions by evaluating them against predefined safety rules, system state conditions, and operational constraints before they can cause deleterious effects, thereby preventing rather than merely responding to harmful outcomes
3Ease of manufacture
If machine-learning-based approaches are used for automated management, then development costs and time are reduced, but the system complexity increases
Solution Approach 1:
The patent designs a universal reinforcement learning framework that can be applied across different distributed computing environments and management scenarios. The RL agent and safety filter constitute a multi-functional system that handles various management tasks including resource allocation, task scheduling, load balancing, and system configuration through a unified approach, reducing the need for environment-specific customizations and lowering overall system complexity
Data Source
AI summary
The current document is directed to a safe-operation-constrained reinforcement-learning-based application manager that can be deployed in various different computational environments, without extensive manual modification and interface development, to manage the computational environments with respect to one or more reward-specified goals. Control actions undertaken by the safe-operation-constrained reinforcement-learning-based application manager are constrained, by stored action filters, to constrain state/action-space exploration by the safe-operation-constrained reinforcement-learning-based application manager to safe actions and thus prevent deleterious impact to the managed computational environment.