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MOEAGAC:an energy aware model with genetic algorithm for efficient scheduling in cloud computing

International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 318-329页

作者： Nageswara Prasadhu Marri N.R.Rajalakshmi CSE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiIndia

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-

关键词： Cloud computing Priority roulette wheel selection Genetic algorithm Population

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Parameters selection of Fitness Scaling in Genetic Algorithm and Its Application

Parameters Selection of Fitness Scaling in Genetic Algorithm...

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2010 Chinese Control and Decision Conference

作者： HAO Guo-Sheng,Yin Yu-Chen,WEI Kai-Xia,GONG Gu,HU Xiao-Ting School of Computer Science and Technology,Xuzhou Normal University,Xuzhou,221116,China

Fitness scaling is an important element affecting the evolutionary performance of genetic *** scaling transformation parameters decide the ***,two conditions for efficient fitness scaling are proposed. The first condition is that the domination relationship should be kept after the transformation;the second condition is that fitness should be different after *** on the two conditions,the formulation with roulette wheel selection is ***,the scopes of parameters of three kind of fitness scaling are *** last,based on the two conditions,the fitness scaling based on logarithm function and triangle function are *** above study of fitness scaling enriches the theory of genetic algorithm.

关键词： genetic algorithm fitness scaling efficiency selective operator roulette wheel selection

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