An Approach for Workflow Scheduling in Cloud Using ACO

Vinothina, V.; Sridaran, R.

doi:10.1007/978-981-10-6620-7_50

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Due to huge amount of user data on the cloud network resources are not utilized properly and in results delay and service failure is occur. To avoid this problem load balancing method is used in which request from the user is distributed among different resources [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient Task Scheduling using Load Balancing in Cloud Computing

Kaur¹,

Dhindsa²

2018

IJANA

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Workflow scheduling is a challenging field in computing in which tasks are scheduled according to the user requirement and it becomes costly due to the quality of service demand by the user. Cloud environment has been deployed for this work so as to reduce the overall cost. To maintain & utilize resources in the cloud computing scheduling mechanism is needed. Many algorithms and protocols are used to manage the parallel jobs and resources which are used to enhance the performance of the CPU in the cloud environment. Particles swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for effective scheduling. This work is based on the optimization of Total execution time and total execution cost. The results of the proposed approach are found to be effective in compare to existing methods. The particle swarm optimization is initialized by using Pareto distribution. TET and TEC illustrated the minimized cost and time by using the GWO to converge the decision of virtual machine. Thus the work concludes that GWO performs better in compare to existing BAT algorithm.

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Section: Introductionmentioning

confidence: 99%