Modular 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 high computational overheads and inefficiencies, making traditional automated management and administration systems impractical, prompting the need for alternative methodologies such as machine-learning-based approaches.
Innovation Solution
A modular reinforcement-learning-based application manager that can be deployed in various computational environments without extensive modification, interfacing with observation and action adapters and metadata for a uniform interface, and featuring a user-specifiable reward-generation interface to tailor feedback and control policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automated management and administration systems are used for distributed computing systems, then system management functionality is provided, but computational overhead and inefficiency increase significantly
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 approaches with machine learning-driven decision-making that adapts to changing system conditions without requiring extensive computational overhead for rule evaluation
Solution Approach 2:
The system dynamically adjusts management parameters and policies based on learned patterns from system observations. The reinforcement learning agent modifies management decisions in response to changing system states, workloads, and performance metrics, enabling adaptive optimization that improves efficiency while maintaining appropriate computational resource usage
2Adaptability or versatility
If the application manager is made adaptable to various computational environments, then deployment flexibility improves, but system complexity increases
Solution Approach 1:
The patent implements a universal application manager architecture that can operate across diverse computational environments including distributed systems, cloud platforms, and edge computing infrastructures. The reinforcement learning agent employs environment-agnostic state representations and action spaces that can be mapped to different underlying systems, enabling a single unified manager to handle multiple environment types without requiring separate specialized systems for each platform
Solution Approach 2:
The system introduces abstraction layers and adapter components that serve as intermediaries between the core reinforcement learning agent and specific computational environments. These adapters translate environment-specific observations and actions into standardized formats understood by the RL agent, while the agent's decisions are translated back into environment-specific commands, thereby reducing complexity by isolating the core learning logic from environment-specific details
Data Source
AI summary
The current document is directed to a modular reinforcement-learning-based application manager that can be deployed in various different computational environments without extensive modification and interface development. The currently disclosed modular reinforcement-learning-based application manager interfaces to observation and action adapters and metadata that provide a uniform and, in certain implementations, self-describing external interface to the various different computational environments which the modular reinforcement-learning-based application manager may be operated to control. In addition, certain implementations of the currently disclosed modular reinforcement-learning-based application manager interface to a user-specifiable reward-generation interface to allow the rewards that provide feedback from the computational environment to the modular reinforcement-learning-based application manager to be tailored to meet a variety of different user expectations and desired control policies.