MOEAGAC:an energy aware model with genetic algorithm for efficient scheduling in cloud computing
作者机构:CSEVel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiIndia
出 版 物:《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊(英文))
年 卷 期:2022年第15卷第2期
页 面:318-329页
核心收录:
学科分类:08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 0713[理学-生态学]
主 题:Cloud computing Priority Roulette wheel selection Genetic algorithm Population
摘 要:Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization *** research aims to propose the optimization of makespan,energy consumption and data transfer time(DTT)by considering the priority *** research work is concentrated on the multi-objective approach based on the genetic algorithm(GA)and energy aware model to increase the efficiency of the task ***/methodology/approach-Cloud computing is the recent advancement of the distributed and cluster *** computing offers different services to the clients based on their requirements,and it works on the environment of *** environment contains the number of data centers which are distributed *** challenges faced by the cloud environment are energy consumption of the data *** scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the *** paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic *** algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual *** energy model is developed for picking the task based on the fitness *** simulation results show the performance of the multi-objective model with respect to makespan,DTT and energy ***-The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual *** directed acyclic graph is used to represent the task *** proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT *** proposed model recorded 125 joules energy consumption for 50 VMs when all are in active ***/value-This paper proposed the multi-