Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2846
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dc.contributor.authorMishra, Sambit Kumar-
dc.contributor.authorSahoo, Bibhudatta-
dc.contributor.authorManikyam, P. Satya-
dc.date.accessioned2018-01-03T04:43:13Z-
dc.date.available2018-01-03T04:43:13Z-
dc.date.issued2017-
dc.identifier.citation3rd International Conference on Communication and Information Processing (ICCIP 2017), Tokyo, Japan, 24 - 26 November, 2017en_US
dc.identifier.urihttp://hdl.handle.net/2080/2846-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractEfficient scheduling of heterogeneous tasks to heterogeneous processors for any application is crucial to attain high performance. Cloud computing provides a heterogeneous environment to perform various operations. The scheduling of user requests (tasks) in the cloud environment is a NP-hard optimization problem. Researchers present various heuristic and metaheuristic techniques to provide the sub-optimal solution to the problem. In this paper, we have proposed an Ant Colony Optimization (ACO) based task scheduling (ACOTS) algorithm to optimize the makespan of the system and reducing the average waiting time. The designed algorithm is implemented and simulated in CloudSim simulator. Results of simulations are compared to Round Robin and Random algorithms which show satisfactory output.en_US
dc.subjectAnt Colony Optimization (ACO)en_US
dc.subjectCloud computingen_US
dc.subjectMakespanen_US
dc.subjectTask schedulingen_US
dc.subjectVMen_US
dc.titleAdaptive Scheduling of Cloud Tasks Using Ant Colony Optimizationen_US
dc.typeArticleen_US
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