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Title |
Optimizing Task Scheduling Using Service Quality Measures and Data-aware Scheduler in Cloud Computing Environment |
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Author |
Ensieh Azmayandeh , Mohammadreza Farahpour Haghani, Elham jafari |
| Citation |
Vol. 25 No. 4 pp. 173-177 |
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Abstract |
Cloud computing can provide flexible resources and dynamic and scalable services through the internet. Considering these dynamic resources, their allocation to tasks is one of the most challenging issues in the cloud computing environment. Accordingly, different scheduling algorithms have been proposed for cloud computing. These algorithms investigate different factors, including the specified service quality by users, execution time, cost, load balancing and energy consumption in task execution, fairness, and utility of resources. This paper aims to propose a method to minimize the completion time and cost using the ant colony algorithm and data-aware scheduler. More specifically, first tasks are categorized into two groups based on completion time and cost, the optimal path is found using the ant colony algorithm, and the most appropriate virtual machine is selected using the data-aware scheduler technique based on bandwidth, the load balancing of the source machine, and the load balancing of the destination machine. Results are presented using cloudsim. |
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Keywords |
Cloud computing, Task scheduling, Service quality, Ant colony, Data-aware scheduler. |
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