PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Abstract

In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.