Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3581
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dc.contributor.authorSwain, Chittaranjan-
dc.contributor.authorSahoo, Manmath Narayan-
dc.contributor.authorSatpathy, Anurag-
dc.date.accessioned2021-09-20T05:48:44Z-
dc.date.available2021-09-20T05:48:44Z-
dc.date.issued2021-09-
dc.identifier.citationInternational Conference on Web Services (ICWS), Sept 5-11, 2021, USAen_US
dc.identifier.urihttp://hdl.handle.net/2080/3581-
dc.descriptionCopyright of this paper is with proceedings publisheren_US
dc.description.abstractThe resource-constrained IoT devices often offload tasks to Fog nodes (FNs) owing to the intermittent WAN delays and multi-hopping by executing at remote cloud servers. An efficient allocation strategy satisfies the users’ requirements by ensuring minimum offloading delays and provides a balanced assignment from the service providers’ (SPs) viewpoint. This paper presents a model called LETO that reduces the total offloading delay for real-time tasks and achieves a balanced assignment across FNs. The overall problem is modeled as a one-to-many matching game with maximum and minimum quotas. Owing to the deferred acceptance algorithm (DAA) inapplicability, we use a proficient version of the DAA called multi-stage deferred acceptance algorithm (MSDA) to obtain a fair and Pareto-optimal assignment of tasks to FNs. Extensive simulations confirm that LETO can achieve a more balanced assignment compared to the baseline algorithms.en_US
dc.subjectLoad Balancing, , Matching Theory,en_US
dc.subjectMax-Min Quotaen_US
dc.subjectIoT, Fog Systems,en_US
dc.subjectTask Offloadingen_US
dc.titleLETO: An Efficient Load Balanced Strategy for Task Offloading in IoT-Fog Systemsen_US
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