Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing

Zhao, Chen; Zhang, Shanshan; Liu, Qingfeng; Xie, Jun; Hu, Junjun

doi:10.1109/wicom.2009.5301850

Cited by 153 publications

(75 citation statements)

References 4 publications

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“…The authors in [36] proposed a load balancing technique based on artificial neural network (ANN), which utilizes back propagation algorithm to distribute as equally as possible the workload among all the servers. In the work proposed in [37] the researchers developed a GA scheduler to schedule independent and divisible tasks in a cloud computing environment, with makespan as an objective. Furthermore, to increase the Quality of Service of the cloud system, the authors in [38] introduced an improved task scheduling algorithm to assign tasks to computing resources with the objective of maximizing the scalability and reliability of the cloud system.…”

Section: Related Workmentioning

confidence: 99%

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Attiya¹,

Zhang²

2017

IJCA

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Recent advances in various areas such as networking, information and communication technologies have greatly boosted the potential capabilities of cloud computing and made it become more prevalent in recent years. Cloud computing is a promising computing paradigm that facilitates the delivery of IT infrastructure, platforms, and applications of any kind to consumers as services over the internet. Although cloud computing systems nowadays provide better ways to accomplish the job requests in terms of responsiveness and scalability under various workloads, scheduling of jobs or tasks in cloud environment is still NPcomplete and complex in nature due to the dynamicity of resources and on-demand user application requirements. In this paper, a simplified version of particle swarm optimization (PSO) algorithm is proposed to solve the job scheduling problem in cloud computing environment. To evaluate the performance of the proposed approach, this study compares the proposed PSO strategy with genetic algorithm (GA), by having both of them implemented on CloudSim toolkit. The results obtained demonstrate that the presented PSO algorithm can significantly reduce the makespan of job scheduling problem compared with the other metaheuristic algorithm evaluated in this paper.

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Section: Related Workmentioning

confidence: 99%

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Attiya¹,

Zhang²

2017

IJCA

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“…Zhao, Zhang, Liu, Xie and Hu (2009) [7] presented an GAbased optimized algorithm for scheduling divisible and autonomous tasks adapting to varying computational and storage needs in heterogeneous systems, where resources are of computational and communication heterogeneity. Ge and Wei (2010) [6] developed a new scheduler, which makes a scheduling decision by evaluating the entire group of tasks in the job queue and uses GA for optimization.…”

Section: Figure 1: Steps Of Genetic Algorithmmentioning

confidence: 99%

An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

Bilgaiyan¹,

Sagnika²,

Das³

2014

IJCA

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Cloud computing is a popular computing paradigm that performs processing of huge volumes of data using highly available geographically distributed resources that can be accessed by users on the basis of Pay As per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. This paper analyzes various evolutionary and swarm based task scheduling algorithms that address the above mentioned problem.

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“…In Literature [6][7], a random integer programming is proposed to optimize resources, and make dynamic allocation of resources by examining resource overhead of various stages. In Literature [8], genetic algorithm, basic ant colony algorithm and resource scheduling algorithm are proposed to allocate cloud computing resources for users reasonably with the target of the completion time of the best overall task.…”

Section: Introductionmentioning

confidence: 99%