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An Enhanced Grey Wolf Optimization Algorithm for Efficient Task Scheduling in Mobile Edge Computing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 56 |
| Year of Publication: 2024 |
| Authors: Jafar Aminu, Rohaya Latip, Zurina Mohd Hanafi, Shafinah Kamarudin, Bashar Umar Kangiwa, Ayuba Liman |
10.5120/ijca2024924287
|
Jafar Aminu, Rohaya Latip, Zurina Mohd Hanafi, Shafinah Kamarudin, Bashar Umar Kangiwa, Ayuba Liman . An Enhanced Grey Wolf Optimization Algorithm for Efficient Task Scheduling in Mobile Edge Computing. International Journal of Computer Applications. 186, 56 ( Dec 2024), 39-44. DOI=10.5120/ijca2024924287
Abstract
Mobile edge computing (MEC) is a fundamental paradigm that brings computational resources closer to end users, minimizing latency and improving performance for real-time applications. Task scheduling optimization is generally a significant difficulty in MEC systems because of the dynamic nature of edge servers, limited processing resources, and energy restrictions. This leads to several problems, such as high energy consumption, makespan, extended task execution durations, and inefficient resource utilization. An enhanced grey wolf optimization method that introduces novel strategies to balance the exploration and exploitation processes more successfully will be used in this study to address these problems. The suggested EGWO algorithm handles the dynamic task allocation for maximum usage of resources, which minimizes makespan and energy consumption. We undertake comprehensive simulations for different workloads and show that EGWO consistently performs better than state-of-the-art techniques like WOA, PSO, and RFOAOA. EGWO leads to significant improvements in energy efficiency and makespan. It is, therefore a reliable and scalable solution for scheduling tasks in the MEC environment
References
- C. Feng, P. Han, X. Zhang, B. Yang, Y. Liu, and L. Guo, “Computation offloading in mobile edge computing networks: A survey,” J. Netw. Comput. Appl., vol. 202, no. April, p. 103366, 2022, doi: 10.1016/j.jnca.2022.103366.
- T. X. Tran, A. Hajisami, P. Pandey, and D. Pompili, “Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges,” IEEE Commun. Mag., vol. 55, no. 4, pp. 54–61, 2017, doi: 10.1109/MCOM.2017.1600863.
- M. E. C. Networks, “A Survey of Energy Optimization Approaches for Computational Task Offloading and Resource Allocation in,” 2023.
- A. Mahjoubi, K. J. Grinnemo, and J. Taheri, “An Efficient Simulated Annealing-based Task Scheduling Technique for Task Offloading in a Mobile Edge Architecture,” Proc. 2022 IEEE Conf. Cloud Netw. 2022, CloudNet 2022, pp. 159–167, 2022, doi: 10.1109/CloudNet55617.2022.9978900.
- A. Muhamad and M. Hussin, “Governing Resource Failures through Reinforcement Learning Scheduling in Fog/Edge Computing: A Review,” 2024 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2024 - Proc., no. June, pp. 256–261, 2024, doi: 10.1109/I2CACIS61270.2024.10649846.
- Y. Siriwardhana, P. Porambage, M. Liyanage, and M. Ylianttila, “A Survey on Mobile Augmented Reality with 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects,” IEEE Commun. Surv. Tutorials, vol. 23, no. 2, pp. 1160–1192, 2021, doi: 10.1109/COMST.2021.3061981.
- M. Sharma, A. Tomar, and A. Hazra, “Edge Computing for Industry 5.0: Fundamental, Applications, and Research Challenges,” IEEE Internet Things J., vol. 11, no. 11, pp. 19070–19093, 2024, doi: 10.1109/JIOT.2024.3359297.
- S. M. Altowaijri, “Workflow Scheduling and Offloading for Servicebased Applications in Hybrid Fog-Cloud Computing,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 12, pp. 726–735, 2021, doi: 10.14569/IJACSA.2021.0121290.
- M. B. Gawali and S. K. Shinde, “Task scheduling and resource allocation in cloud computing using a heuristic approach,” J. Cloud Comput., vol. 7, no. 1, 2018, doi: 10.1186/s13677-018-0105-8.
- K. K. Mishra, “Grey Wolf Optimization,” Nature-Inspired Algorithms, pp. 131–143, 2022, doi: 10.1201/9781003313649-6.
- D. Gabi et al., “Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme,” Neural Comput. Appl., vol. 34, no. 16, pp. 14085–14105, 2022, doi: 10.1007/s00521-022-07260-y.
- Z. Tang, W. Jia, X. Zhou, W. Yang, and Y. You, “Representation and Reinforcement Learning for Task Scheduling in Edge Computing,” IEEE Trans. Big Data, vol. 8, no. 3, pp. 795–808, 2022, doi: 10.1109/TBDATA.2020.2990558.
- L. Gu, J. Cai, D. Zeng, Y. Zhang, H. Jin, and W. Dai, “Energy efficient task allocation and energy scheduling in green energy powered edge computing,” Futur. Gener. Comput. Syst., vol. 95, pp. 89–99, 2019, doi: 10.1016/j.future.2018.12.062.
- Y. Kim, C. Song, H. Han, H. Jung, and S. Kang, “Collaborative Task Scheduling for IoT-Assisted Edge Computing,” IEEE Access, vol. 8, pp. 216593–216606, 2020, doi: 10.1109/ACCESS.2020.3041872.
- Q. Zhang, X. Lin, Y. Hao, and J. Cao, “Energy-Aware Scheduling in Edge Computing Based on Energy Internet,” IEEE Access, vol. 8, pp. 229052–229065, 2020, doi: 10.1109/ACCESS.2020.3044932.
- J. Li et al., “Multiobjective Oriented Task Scheduling in Heterogeneous Mobile Edge Computing Networks,” IEEE Trans. Veh. Technol., vol. 71, no. 8, pp. 8955–8966, 2022, doi: 10.1109/TVT.2022.3174906.
- J. Wang and D. Li, “Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing,” Sensors (Switzerland), vol. 19, no. 5, 2019, doi: 10.3390/s19051023.
- N. Issa, Z. Alaa, and I. Abed, “Solving Suggested Problems using Grey Wolf Optimization,” 2022, doi: 10.4108/eai.7-9-2021.2314893.
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