Resource Aware Monitoring in Distributed System using Tabu Search Algorithm

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