Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3517
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dc.contributor.authorKurroliya, Kuldeep-
dc.contributor.authorMohanty, Sagarika-
dc.contributor.authorSahoo, Bibhudatta-
dc.contributor.authorKanodia, Khushboo-
dc.date.accessioned2020-03-04T11:53:20Z-
dc.date.available2020-03-04T11:53:20Z-
dc.date.issued2020-02-
dc.identifier.citation7th International Conference on Signal Processing and Integrated Networks (SPIN 2020), Amity University, Noida, Delhi, India, 27-28 February 2020en_US
dc.identifier.urihttp://hdl.handle.net/2080/3517-
dc.descriptionCopyright belongs to proceedings publisheren_US
dc.description.abstractSoftware-Defined Network (SDN) isolates the data plane networking equipment from the control plane to speed up the quick distribution, composition, and growth of networks. Controllers in the software-defined network play an important role like managing the tasks performed by the switches which come at a tradeoff of the aim of maximizing the energy saving in SDN. If more edges of SDN networks are switched off, it may lead to select the routes where propagation delays will be larger. In this regard, we refer to the task of allocating switches to the controller with the target of making the maximum number of inactive edges while satisfying the latency. In this paper, we propose a link based genetic algorithm(LBGA) and compared this with Greco. From the evaluation, it is observed that the results of various topologies have shown energy savings up to more than 55% during the hours when the edges are inactive and this is better in comparison to Greco.en_US
dc.subjectSoftware Defined Network(SDN)en_US
dc.subjectEnergy Savingen_US
dc.subjectLatencyen_US
dc.subjectLink based Genetic Algorithm(LBGA)en_US
dc.titleMinimizing Energy Consumption in Software Defined Networksen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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