Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ACM Transactions on Computer Systems (TOCS)
Chisel: A Policy-Driven, Context-Aware, Dynamic Adaptation Framework
POLICY '03 Proceedings of the 4th IEEE International Workshop on Policies for Distributed Systems and Networks
A middleware for context-aware agents in ubiquitous computing environments
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
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This paper suggests a context adaptive self-configuration system, in which the system itself is aware of its available resources, and executes a configuration suitable for each system and the characteristics of each user. A great deal of time and effort is required to manually manage computing systems, which are becoming increasingly larger and complicated. Many studies have dealt with these problems but most unfortunately have focused on ‘automation’, which do not reflect the system specifications that differ between systems. Therefore, this paper proposes an adaptive self-configuration system that collects diverse context information for a user and its system, reflects it to an auto-response file and executes the detail parameter setting automatically. A prototype was developed to evaluate the system and compare the results with the conventional methods of manual configuration and MS-IBM systems. The results confirmed the effectiveness of the proposed system.