Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

Abstract

Cloud computing technology enables sharing of computer system resources among users through internet. Many numbers of users may request for sharable resources from a cloud. The sharable resources must be effectively distributed among requested users with in a less amount of time. Task scheduling is one of the ways of handling the user requests effectively in a cloud environment. There were many existing biologically inspired optimization techniques worked with task scheduling problems. The proposed paper is aimed at clubbing clustering techniques with biologically inspired optimization algorithms for deriving better results. A new hybrid methodology KPSOW (K-means with PSO using weights) has been proposed in the paper, which makes use of the strengths of both the Kmeans and PSO algorithms with the inclusion of weights concept. The results have shown that KPSOW has made considerable changes in reducing the makespan and improves the utilization of computing resources in the cloud.

Most Valuable Player Algorithm for Solving Minimum Vertex Cover Problem

“…In general, many heuristic and metaheuristic algorithms were used to solve many optimization problems; such as Minimum Vertex Cover Problem (MVCP), Traveling Salesman Problem (TSP), 15 puzzle problem, task scheduling, software testing, and non-optimization problems [1][2][3][4]. Examples of heuristic algorithms are A* heuristic search algorithm and iterative deepening A* (IDA*) heuristic search algorithm [5].…”

“…The MVCP is the problem of finding a subset S such that |S| is the minimum. The MVCP can be formulated as in (1).…”