Paper Info
Title
Cost optimized Hybrid Genetic-Gravitational Search Algorithm for load scheduling in Cloud Computing.
Abstract
In cloud computing, cost optimization is a prime concern for load scheduling. The swarm based meta-heuristics are prominently used for load scheduling in distributed computing environment. The conventional load scheduling approaches require a lot of resources and strategies which are non-adaptive and static in the computation, thereby increasing the response time, waiting time and the total cost of computation. The swarm intelligence-based load scheduling is adaptive, intelligent, collective, random, decentralized, self-collective, stochastic and is based on biologically inspired mechanisms than the other conventional mechanisms. The genetic algorithm schedules the particles based on mutation and crossover techniques. The force and acceleration acting on the particle helps in the finding the velocity and position of the next particle. The best position of the particles is assigned to cloudlets to be executed on the virtual machines in the cloud. The paper proposes a new load scheduling technique, Hybrid Genetic-Gravitational Search Algorithm (HG-GSA) for reducing the total cost of computation. The total computational cost includes cost of execution and transfer. It works on hybrid crossover technique based gravitational search algorithm for searching the best position of the particle in the search space. The best position of the particle is used calculating the force. The HG-GSA is compared to the existing approaches in the CloudSim simulator. By the convergence and statistical analysis of the results, the proposed HG-GSA approach reduces the total cost of computation considerably as compared to existing PSO, Cloudy-GSA and LIGSA-C approaches.
Year
DOI
Venue
2019
10.1016/j.asoc.2019.105627
Applied Soft Computing
Keywords
Field
DocType
Cloud computing,Load scheduling,Gravitational Search Algorithm,Swarm intelligence,Genetic algorithm
Mathematical optimization,Crossover,Search algorithm,Swarm behaviour,Swarm intelligence,Schedule,Total cost,Mathematics,Genetic algorithm,Cloud computing
Journal
Volume
ISSN
Citations 
83
1568-4946
2
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Divya Chaudhary120.34
Bijendra Kumar2588.10