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Analytical model for task offloading in a fog computing system with batch-size-dependent service
Islamic Azad University, Iran.
National Yunlin University of Science and Technology, Taiwan.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-4473-7763
Shahed University, Iran.
2022 (English)In: Computer Communications, ISSN 0140-3664, E-ISSN 1873-703X, Vol. 190, p. 201-215Article in journal (Refereed) Published
Abstract [en]

Task offloading is one of the main concepts in fog computing which improves the system efficiency and decreases latency. Previously proposed models, such as exponential queue models, addressed the offloading models in a simple model. This study proposes a novel analytical model that examines batch queuing systems and the influence of batch size-dependent service time on system performance. Some of the system's properties are indicated using this model, and the correctness of the suggested model via numerical evaluations and simulations is shown. The evaluation results show that our proposed model provides acceptable accuracy and enables efficient task offloading, applied to fog computing systems. 

Place, publisher, year, edition, pages
Elsevier B.V. , 2022. Vol. 190, p. 201-215
Keywords [en]
Analytical approximation model, Batch processing, Fog computing, Task offloading, Analytical models, Batch data processing, Fog, Analytical approximation, Approximation modeling, Batch sizes, Computing system, Exponentials, Queue models, Size dependent, System efficiency
National Category
Economics
Identifiers
URN: urn:nbn:se:ri:diva-59215DOI: 10.1016/j.comcom.2022.04.010Scopus ID: 2-s2.0-85129703685OAI: oai:DiVA.org:ri-59215DiVA, id: diva2:1667536
Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-06-10Bibliographically approved

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Balador, Ali

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