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http://hdl.handle.net/2080/3581
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DC Field | Value | Language |
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dc.contributor.author | Swain, Chittaranjan | - |
dc.contributor.author | Sahoo, Manmath Narayan | - |
dc.contributor.author | Satpathy, Anurag | - |
dc.date.accessioned | 2021-09-20T05:48:44Z | - |
dc.date.available | 2021-09-20T05:48:44Z | - |
dc.date.issued | 2021-09 | - |
dc.identifier.citation | International Conference on Web Services (ICWS), Sept 5-11, 2021, USA | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/3581 | - |
dc.description | Copyright of this paper is with proceedings publisher | en_US |
dc.description.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. | en_US |
dc.subject | Load Balancing, , Matching Theory, | en_US |
dc.subject | Max-Min Quota | en_US |
dc.subject | IoT, Fog Systems, | en_US |
dc.subject | Task Offloading | en_US |
dc.title | LETO: An Efficient Load Balanced Strategy for Task Offloading in IoT-Fog Systems | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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MS_ICWS2021.pdf | 328.12 kB | Adobe PDF | View/Open |
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