Context awarable self-configuration system for distributed resource management

Authors:
Seunghwa Lee;Eunseok Lee
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Chunchun jangahn Suwon, Korea;School of Information and Communication Engineering, Sungkyunkwan University, Chunchun jangahn Suwon, Korea
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

Citing 2
Cited 0

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining

ACM Transactions on Computer Systems (TOCS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Today's system administrator is forced to perform individual configuration and maintenance tasks (i.e. installation, reconfiguration, update) on numerous systems, in various formats. These tasks are time-consuming and labor intensive. Several research projects have attempted to resolve these issues with the development of an integrated, centralized management system. However, many tasks are still left to the system administrator for manual handling. A customized configuration system that reflects comprehensive context has not yet been fully realized. This paper proposes a context aware self-configuration system by employing multi-agents to collectively gather contextual information based on the system resources and user's system usage patterns. This proposed system, then analyzes the collected information and performs automatic configuration as and when required. This system will allow not only enable automation of the previously manual tasks, but also allow, in effect, a more customized configuration.