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Copyright © 2022 Muhammad Mazhar Bukhari et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

Abstract

Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.

Details

Title
An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression
Author
Muhammad Mazhar Bukhari 1 ; Ghazal, Taher M 2 ; Sagheer Abbas 1   VIAFID ORCID Logo  ; Khan, M A 3   VIAFID ORCID Logo  ; Umer Farooq 4 ; Wahbah, Hasan 5 ; Ahmad, Munir 1   VIAFID ORCID Logo  ; Khan, Muhammad Adnan 6   VIAFID ORCID Logo 

 Department of Computer Science, National College of Business Administration and Economics, Lahore 54660, Pakistan 
 Center for Cyber Security Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia; School of Information Technology Skyline University College, University City Sharjah, Sharjah 1797, UAE 
 Riphah School of Computing & Innovation Faculty of Computing, Riphah International University Lahore Campus, Lahore 54000, Pakistan 
 Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan 
 College of Computer Information Technology, American University in Emirates, Dubai, UAE 
 Pattern Recognition and Machine Learning Lab Department of Software, Gachon University, Seongnam 13557, Republic of Korea 
Editor
Carlos M Travieso-González
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
ISSN
16875265
e-ISSN
16875273
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2625916178
Copyright
Copyright © 2022 Muhammad Mazhar Bukhari et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/