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Hybrid Bee Ant Colony Algorithm for Effective Load Balancing And Job Scheduling In Cloud Computing

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dc.contributor.author Yeboah, Thomas,OdabiOdabi I
dc.date.accessioned 2016-06-22T11:19:53Z
dc.date.accessioned 2022-01-16T06:45:22Z
dc.date.available 2016-06-22T11:19:53Z
dc.date.available 2022-01-16T06:45:22Z
dc.date.issued 2016-06-22
dc.identifier.issn 2016020
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/445
dc.description.abstract Cloud Computing is new paradigm in computing that promises a delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). Cloud Computing is a new style of computing on the internet. It has many merits along with some crucial issues that need to be resolved in order to improve reliability of cloud environment. These issues are related with the load balancing, fault tolerance and different security issues in cloud environment. In this paper the main concern is to develop an effective load balancing algorithm that gives satisfactory performance to both, cloud users and providers. This proposed algorithm (hybrid Bee Ant Colony algorithm) is a combination of two dynamic algorithms: Ant Colony Optimization and Bees Life algorithm. Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues whiles the Bees Life algorithm is used for optimization of job scheduling in cloud environment. The results of the proposed algorithm shows that the hybrid Bee Ant Colony algorithm outperforms the performances of both Ant Colony algorithm and Bees Life algorithm when evaluating the proposed algorithm performances en_US
dc.language.iso en en_US
dc.subject Ant Colony Optimization algorithm, Bees Life algorithm, Scheduling Algorithm, Performance, cloud computing, load balancing, Waiting time, Response time en_US
dc.title Hybrid Bee Ant Colony Algorithm for Effective Load Balancing And Job Scheduling In Cloud Computing en_US
dc.type Article en_US


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