Improved cost-based algorithm for task scheduling in cloud computing

Selvarani, S.; Sadhasivam, G. Sudha

doi:10.1109/iccic.2010.5705847

Cited by 184 publications

(69 citation statements)

References 5 publications

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“…This scheduling algorithm measures both resource cost and computation performance. It improves the computation /communication ratio by grouping the user's tasks according to a particular cloud resource's processing capability and sends the grouped jobs to the resources [19].…”

Section: Related Workmentioning

confidence: 99%

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

Nagar¹,

Gupta²,

Singh³

2018

IJITCS

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Abstract-Cloud computing is service based technology on internet which facilitates users to access plenty of resources on demand from anywhere and anytime in a metered manner i.e. pay per usage without paying much heed to the maintenance and implementation details of application. As cloud technology is evolving day by day it is being confronted by numerous challenges, such as time and cost under deadline constraints. Research work done so far mainly focused on reducing cost as well as execution time. In order to minimize cost and execution time previously existing workflow scheduling model known as predict earliest finish time is used. In this research work we have proposed a new PEFT genetic algorithm approach to further reduce the execution time on this model. A strategy is developed to let GA focus on to optimize chromosomes objective to get best suitable mutated children. After obtaining a feasible solution, the genetic algorithm focuses on optimizing the execution time. Experimental results show that our algorithm can find better solution within lesser time.

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Section: Related Workmentioning

confidence: 99%

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

Nagar¹,

Gupta²,

Singh³

2018

IJITCS

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“…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. In summary, by using appropriate and effective scheduling schemes, the execution time of the tasks can be minimized and the cloud resources can be fully utilized, which finally increases the availability and scalability of the entire 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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“…The task will be put in queue at the end and will be taken again when it will reach front and remaining execution will be carried out. The algorithm calculates expected execution time as well balances the load C. Improved Cost-Based Algorithm For Task Scheduling [7] Mrs. Selvarani [7] proposed improved cost based algorithm so to make efficient mapping of tasks to resources which are available. The algorithm divides all tasks into 3 different lists depending on priority of each task.…”

Section: B Round Robin Algorithm[6]mentioning

confidence: 99%

A Review Paper on Various Task Scheduling Algorithms in Cloud Computing

Kaur

2017

IJARCSSE

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Abstract-Cloud Computing is a computing environment where different services are provided to users over the Web. Task Scheduling is one of the important aspects of Cloud Computing, improving the performance of the cloud system. Task Scheduling involves assignment of resources to a particular task for the task to be completed within possible minimum time. Task Scheduling helps in achieving efficient utilization of resources. This paper presents a review of various task scheduling algorithms in Cloud Computing.

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Improved cost-based algorithm for task scheduling in cloud computing

Cited by 184 publications

References 5 publications

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

A Review Paper on Various Task Scheduling Algorithms in Cloud Computing

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