Transferable Training for Reinforcement Learning Application Managers
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Solution Overview
Problem
The management and administration of complex distributed computing systems are becoming increasingly inefficient due to scaling issues, including communication overheads and component failures, making traditional automated management systems impractical, and there is a need for more effective optimization and cost reduction in administration-and-management control systems.
Innovation Solution
The transfer of training from one automated reinforcement-learning-based application manager to another allows for the automated generation of applications from application components, enabling the composition of reinforcement-learning-based control-and-learning constructs to facilitate efficient management and optimization of distributed computing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional automated management systems are used for distributed computing systems, then system management functionality is provided, but the systems become increasingly inefficient due to scaling issues, communication overheads, and component failures
Solution Approach 1:
The patent replaces traditional rule-based and manual management systems with reinforcement learning-based automated management. The RL agent learns optimal management policies through interaction with the distributed system environment, substituting mechanical control approaches with intelligent adaptive control that can handle scaling issues and communication overheads more effectively
Solution Approach 2:
The patent employs transfer learning to transfer knowledge between different distributed computing systems by adjusting and adapting learned policies to new environments. This allows the management system to maintain efficiency when system parameters such as scale, configuration, or workload characteristics change, avoiding retraining from scratch
2Extent of automation
If reinforcement learning is applied to manage complex computational environments, then automated administration functionality is expanded and development costs decrease, but extensive training time and computational resources are required
Solution Approach 1:
The patent pre-trains reinforcement learning agents on simulated distributed computing environments before deploying them to real systems. This preliminary training in controlled simulation settings allows the agents to learn fundamental management policies without consuming extensive resources on actual production systems, reducing both training time and operational risk
Solution Approach 2:
The patent uses simulation environments as copies of real distributed computing systems for training purposes. These simulated environments replicate system behavior and characteristics, allowing extensive training to be performed on inexpensive copies rather than on the actual production systems, thereby reducing training time and resource consumption
3Adaptability or versatility
If reinforcement learning agents are trained from scratch for each distributed computing system, then system-specific optimization is achieved, but development costs and training resources increase significantly
Solution Approach 1:
The patent develops universal reinforcement learning agents that can be deployed across multiple types of distributed computing systems. These agents learn transferable policies that adapt to different system configurations and workloads, providing system-specific optimization without requiring separate training for each system, thereby reducing development costs and resource requirements
Solution Approach 2:
The patent introduces simulation environments as intermediary training platforms between generic RL training and deployment on specific production systems. These simulations serve as mediators that allow agents to learn transferable policies applicable across different system types, reducing the need for extensive system-specific training while maintaining adaptability
Data Source
AI summary
The current document is directed to transfer of training received by a first automated reinforcement-learning-based application manager while controlling a first application is transferred to a second automated reinforcement-learning-based application manager which controls a second application different from the first application. Transferable training provides a basis for automated generation of applications from application components. Transferable training is obtained from composition of applications from application components and composition of reinforcement-learning-based-control-and-learning constructs from reinforcement-learning-based-control-and-learning constructs of application components.