Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

Abstract

Cloud datacenter (Dc) have become popular in recent years with the rising popularity and high performance of cloud computing. The multi-step of data computation and diverse task dependencies fail in the task, energy consumption, overloading of Virtual Machines (VMs), and violation of the agreement. To overcome these challenges, we propose a genetic algorithm (GA) based multiphase fault tolerance (MFTGA) approach for intelligently schedule the tasks over the VMs for multiuser. This MFTGA approach efficiently maps optimal VMs with users according to the service level agreement (SLA). The presented approach comprises four phases namely individual phase, local phase, global phase, and fault tolerance phase. In the individual phase of the MFTGA algorithm, we calculate the local fitness (fl) of each user. Then calculate the global fitness (fg) of multiuser according to the SLA in the global fitness phase. After mapping the optimal VMs with the multiuser, we check the status of task execution in the fault tolerance phase. MFTGA method is used to improve the reliability, latency, and reduce the failure of the task in the cloud computing environment. The proposed MFTGA scheme is compared against the GA and Adoptive Incremental Genetic Algorithm (AIGA). The simulation results validate that the proposed method exhibits better performance than GA and AIGA in terms of execution time, memory utilization, cost, SLA violation, and energy consumption.