Abstract
Edge-cloud has drawn a significant attention of the IT industries, cloud operators as well as the network service providers and mobile users. To meet the user requirements the service providers have been able to provide various types of services related to the network by applying virtual network functions and deployed cloudlet in Metropolitan Area Network. Our goal is to maximize the number of admitted requests via load balancing by reducing the integrated cost of admissions within a time spare. A problem function is designed to offload set of the tasks in a metropolitan area network, where a particular network related function with a maximum bearable delay is requested by each offloaded task through different network services according to different offloading requests. An algorithm is designed to make a reduced bipartite graph with maximum matching and minimum weight. To save cost we develop an effective prediction algorithm where instances of network functions get created and/or released in different cloudlets considering that the offloading request patterns dynamically changes. Later we have also analyzed that the possible outcome of the proposed algorithm is efficient while distributing the load among the cloudlets into the edge-cloud.
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Acknowledgement
Department of Science and Technology (DST) for sanctioning a research Project entitled “Dynamic Optimization of Green Mobile Networks: Algorithm, Architecture and Applications” under Fast Track Young Scientist scheme reference no.: SERB/F/5044/2012-2013, DST FISTSR/FST/ETI-296/ and TEQIP-III.
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Guha Roy, D., Ghosh, A., Mahato, B., De, D. (2019). QoS-Aware Task Offloading Using Self-organized Distributed Cloudlet for Mobile Cloud Computing. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_32
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DOI: https://doi.org/10.1007/978-981-13-8578-0_32
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