Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3581
Title: LETO: An Efficient Load Balanced Strategy for Task Offloading in IoT-Fog Systems
Authors: Swain, Chittaranjan
Sahoo, Manmath Narayan
Satpathy, Anurag
Keywords: Load Balancing, , Matching Theory,
Max-Min Quota
IoT, Fog Systems,
Task Offloading
Issue Date: Sep-2021
Citation: International Conference on Web Services (ICWS), Sept 5-11, 2021, USA
Abstract: The 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.
Description: Copyright of this paper is with proceedings publisher
URI: http://hdl.handle.net/2080/3581
Appears in Collections:Conference Papers

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