Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8129
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dc.contributor.authorYadav, V.-
dc.contributor.authorNatesha, B.V.-
dc.contributor.authorRam Mohana Reddy, Guddeti-
dc.date.accessioned2020-03-30T10:18:06Z-
dc.date.available2020-03-30T10:18:06Z-
dc.date.issued2019-
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2019-October, , pp.1280-1285en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8129-
dc.description.abstractInternet of Thing (IoT) applications require an efficient platform for processing big data. Different computing techniques such as Cloud, Edge, and Fog are used for processing big data. The main challenge in the fog computing environment is to minimize both energy consumption and makespan for services. The service allocation techniques on a set of virtual machines (VMs) is the decidable factor for energy consumption and latency in fog servers. Hence, the service allocation in fog environment is referred to as NP-hard problem. In this work, we developed a hybrid algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to solve this NP-hard problem. The proposed GA-PSO is used for optimal allocation of services while minimizing the total makespan, energy consumption for IoT applications in the fog computing environment. We implemented the proposed GA-PSO using customized C simulator, and the results demonstrate that the proposed GA-PSO outperforms both GA and PSO techniques when applied individually. � 2019 IEEE.en_US
dc.titleGA-PSO: Service Allocation in Fog Computing Environment Using Hybrid Bio-Inspired Algorithmen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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