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Resource Aware Monitoring in Distributed System using Tabu Search Algorithm

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 23
Year of Publication: 2014
Authors:
Sonali L. Vidhate
M. U. Kharat
10.5120/16934-7000

Sonali L Vidhate and M U Kharat. Article: Resource Aware Monitoring in Distributed System using Tabu Search Algorithm. International Journal of Computer Applications 96(23):22-25, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Sonali L. Vidhate and M. U. Kharat},
	title = {Article: Resource Aware Monitoring in Distributed System using Tabu Search Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {23},
	pages = {22-25},
	month = {June},
	note = {Full text available}
}

Abstract

Tabu search algorithm like simulated annealing or evolutionary algorithm or genetic algorithm and guided local search algorithm is a effective solution of optimization problem. This is the most comprehensive combinatorial optimization technique available for treating difficult problems. It is a neighborhood based search method which is very useful in distributed system for monitoring application. Distributed operation of Applications involve: Multiple applications deployed over different sets of hosts e. g. Datacenters. Application State monitored the performance of both systems and applications running on large-scale distributed systems. It is constantly collecting detailed performance attribute values as a large number of nodes & a large number of attributes. Tricky task of Resource aware application state monitoring is the monitoring overlay construction. In this method first, it jointly considers inter-task cost sharing opportunity and node-level resource constraints. Further, it clearly models the per-message processing overhead which can be extensive but is often ignored by earlier works. Second, REMO produces a forest of optimized monitoring trees through iterations of two phases. One stage explores cost-sharing opportunities between tasks, and the other refines the tree with resource-sensitive construction schemes. REMO also included an adaptive algorithm that balances the profit and costs of cover adaptation. This is helpful for large systems with continuously changing monitoring tasks.

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