Meta-heuristic-based offloading task optimization in mobile edge computing
With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decis...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2021-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211023021 |
| _version_ | 1797725251808788480 |
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| author | Aamir Abbas Ali Raza Farhan Aadil Muazzam Maqsood |
| author_facet | Aamir Abbas Ali Raza Farhan Aadil Muazzam Maqsood |
| author_sort | Aamir Abbas |
| collection | DOAJ |
| description | With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks. |
| first_indexed | 2024-03-12T10:28:29Z |
| format | Article |
| id | doaj.art-261d4ac14ac745a29de93987baec3542 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T10:28:29Z |
| publishDate | 2021-06-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-261d4ac14ac745a29de93987baec35422023-09-02T09:26:24ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772021-06-011710.1177/15501477211023021Meta-heuristic-based offloading task optimization in mobile edge computingAamir Abbas0Ali Raza1Farhan Aadil2Muazzam Maqsood3Computer Science Department, COMSATS University Islamabad, Islamabad, PakistanDepartment of Computer Science, University of Engineering and Technology, Taxila, Taxila, PakistanComputer Science Department, COMSATS University Islamabad, Islamabad, PakistanComputer Science Department, COMSATS University Islamabad, Islamabad, PakistanWith the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.https://doi.org/10.1177/15501477211023021 |
| spellingShingle | Aamir Abbas Ali Raza Farhan Aadil Muazzam Maqsood Meta-heuristic-based offloading task optimization in mobile edge computing International Journal of Distributed Sensor Networks |
| title | Meta-heuristic-based offloading task optimization in mobile edge computing |
| title_full | Meta-heuristic-based offloading task optimization in mobile edge computing |
| title_fullStr | Meta-heuristic-based offloading task optimization in mobile edge computing |
| title_full_unstemmed | Meta-heuristic-based offloading task optimization in mobile edge computing |
| title_short | Meta-heuristic-based offloading task optimization in mobile edge computing |
| title_sort | meta heuristic based offloading task optimization in mobile edge computing |
| url | https://doi.org/10.1177/15501477211023021 |
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