Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing
The exponential increase in the Internet of Things (IoT) has affected the cloud computing with increase transmission latency and network overhead for real-time applications. Cloud-fog computing paradigm tackle these limitations by moving computational services closer to the network edge i.e., fog no...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10778557/ |
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| author | Asad Ali Nazia Azim Mohamed Tahar Ben Othman Ateeq Ur Rehman Masoud Alajmi Mosleh Hmoud Al-Adhaileh Faheem Ullah Khan Mamyrbayev Orken Habib Hamam |
| author_facet | Asad Ali Nazia Azim Mohamed Tahar Ben Othman Ateeq Ur Rehman Masoud Alajmi Mosleh Hmoud Al-Adhaileh Faheem Ullah Khan Mamyrbayev Orken Habib Hamam |
| author_sort | Asad Ali |
| collection | DOAJ |
| description | The exponential increase in the Internet of Things (IoT) has affected the cloud computing with increase transmission latency and network overhead for real-time applications. Cloud-fog computing paradigm tackle these limitations by moving computational services closer to the network edge i.e., fog nodes, enhancing the speed of real-time applications. This architecture, with its dynamic computing environment and diverse IoT devices and tasks, demands a reliable and energy-efficient communication network. Joint optimization of computation offloading and task scheduling is a primary challenge, as it involves offloading tasks to optimal computational resources and scheduling them in an efficient order for operational efficacy. While offloading tasks to fog nodes reduces delay but raises energy utilization, offloading them to cloud servers reduces energy usage but raises computational costs and latency. Additionally, inefficient order of task execution (executing lower priority jobs before higher priority tasks) can disrupt system stability and reliability. Therefore, an effective joint optimal computation offloading and task scheduling strategy is essential. To this end, we propose a Multi-objective Arithmetic Optimization-based joint computation offloading and task scheduling algorithm, aiming to minimize energy consumption and transmission latency. Extensive simulations in MATLAB demonstrate the efficacy of the proposed algorithm in terms of designated optimization objectives. |
| format | Article |
| id | doaj-art-98aad5371e6d45f38981ee847578af58 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-98aad5371e6d45f38981ee847578af582024-12-14T00:01:22ZengIEEEIEEE Access2169-35362024-01-011218415818417810.1109/ACCESS.2024.351219110778557Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog ComputingAsad Ali0https://orcid.org/0009-0007-5930-2379Nazia Azim1Mohamed Tahar Ben Othman2https://orcid.org/0000-0002-0990-5805Ateeq Ur Rehman3https://orcid.org/0000-0001-5203-0621Masoud Alajmi4https://orcid.org/0000-0002-1887-6719Mosleh Hmoud Al-Adhaileh5https://orcid.org/0000-0002-3519-1121Faheem Ullah Khan6Mamyrbayev Orken7https://orcid.org/0000-0001-8318-3794Habib Hamam8https://orcid.org/0000-0002-5320-1012Department of Computer Science, Mardan Institute of Science and Technology, Mardan, PakistanDepartment of Computer Science, Abdul Wali Khan University Mardan, Mardan, PakistanDepartment of Computer Science, College of Computer, Qassim University, Buraydah, Saudi ArabiaSchool of Computing, Gachon University, Seongnam-si, Republic of KoreaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of E-Learning and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Software Engineering, University of Science and Technology, Bannu, PakistanDepartment of Computer Science, Institute of Information and Computational Technologies, Almati, KazakhstanFaculty of Engineering, Université de Moncton, Moncton, NB, CanadaThe exponential increase in the Internet of Things (IoT) has affected the cloud computing with increase transmission latency and network overhead for real-time applications. Cloud-fog computing paradigm tackle these limitations by moving computational services closer to the network edge i.e., fog nodes, enhancing the speed of real-time applications. This architecture, with its dynamic computing environment and diverse IoT devices and tasks, demands a reliable and energy-efficient communication network. Joint optimization of computation offloading and task scheduling is a primary challenge, as it involves offloading tasks to optimal computational resources and scheduling them in an efficient order for operational efficacy. While offloading tasks to fog nodes reduces delay but raises energy utilization, offloading them to cloud servers reduces energy usage but raises computational costs and latency. Additionally, inefficient order of task execution (executing lower priority jobs before higher priority tasks) can disrupt system stability and reliability. Therefore, an effective joint optimal computation offloading and task scheduling strategy is essential. To this end, we propose a Multi-objective Arithmetic Optimization-based joint computation offloading and task scheduling algorithm, aiming to minimize energy consumption and transmission latency. Extensive simulations in MATLAB demonstrate the efficacy of the proposed algorithm in terms of designated optimization objectives.https://ieeexplore.ieee.org/document/10778557/Task offloadingcomputation offloadingoptimization algorithmscloud-fog computingarithmetic optimization |
| spellingShingle | Asad Ali Nazia Azim Mohamed Tahar Ben Othman Ateeq Ur Rehman Masoud Alajmi Mosleh Hmoud Al-Adhaileh Faheem Ullah Khan Mamyrbayev Orken Habib Hamam Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing IEEE Access Task offloading computation offloading optimization algorithms cloud-fog computing arithmetic optimization |
| title | Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing |
| title_full | Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing |
| title_fullStr | Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing |
| title_full_unstemmed | Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing |
| title_short | Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing |
| title_sort | joint optimization of computation offloading and task scheduling using multi objective arithmetic optimization algorithm in cloud fog computing |
| topic | Task offloading computation offloading optimization algorithms cloud-fog computing arithmetic optimization |
| url | https://ieeexplore.ieee.org/document/10778557/ |
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