A Hybrid Slap Swarm and Harris Hawks Optimization for Secure Task Scheduling in Cloud Environment based on Multi-Objective Function

Abstract

                Task scheduling is the prime significant parameter in cloud computing (CC) which plays a major role in the effectiveness of the entire CC facilities. In the cloud environment (CE), the submitted tasks are executed on proper time using the accessible resources in order to accomplish appropriate resource utilization, efficiency and low makes pan which involves effective task scheduling (TS) procedure for accurate task distribution. This paper develops a hybrid slap swarm and Harris hawks optimization for secure Task Scheduling in Cloud Environment (TSCE) based on multi-objective (MO) function. For improving the scheduling process, Slap swarm (SS) and Harris hawks’ optimization (HHO) procedure is hybridized as SSHHO to solve the optimization difficult. These two algorithms are successfully merged to perform the task allocation process. The proposed SSHHO algorithm considers the multi-objective function like makes pan, resource utilization, energy consumption, final task weight and round trip time latency for the scheduling process. Using the Clouds environment, the proposed SSHHO algorithm is evaluated and the performance of the proposed system is related to the existing algorithms of CSPO, HGAACO, FMPSO, MSDE and HGPSO with different multi-objective constraints. By evaluating the outcome, the developed SSHHO process achieves maximum efficiency, less energy consumption, low makes pan, and maximum resource utilization compared to existing algorithms.