Improving Resource Allocation in Mobile Edge Computing Using Particle Swarm and Gray Wolf Optimization Algorithms
Subject Areas : ICT
seyed ebrahim dashti
1
*
,
saeid shabooei
2
1 -
2 - Islamic Azad university
Keywords: Resource Allocation Improvement, Mobile Edge Computing, Particle Swarm Optimization Algorithm, Gray Wolf Algorithm,
Abstract :
Mobile edge computing improves the experience of end users to achieve appropriate services and service quality. In this paper, the problem of improving resource allocation, when offloading tasks, based on mobile devices to edge servers in computing systems is investigated. Some tasks are uploaded and processed locally and some to edge servers. The main issue is that the offloaded tasks for virtual machines in computing networks are properly scheduled to minimize computing time, service cost, computing network waste, and the maximum connection of a task with the network. In this paper, a multi-objective hybrid algorithm of particle swarm and gray wolf was introduced to manage resource allocation and task scheduling to achieve an optimal result in edge computing networks. Local search in the particle swarm algorithm has good results in the problem, but it will cause the loss of global optima, so in this problem, in order to improve the model, the gray wolf algorithm was used as the main basis of the proposed algorithm, in the wolf algorithm Gray, due to the graphical approach to the problem, the set of global searches will reach the optimal solution, so by combining these functions, we tried to improve the operational conditions of the two algorithms for the desired goals of the problem. In order to create a network in this research, the network creation parameters in the basic article were used and the LCG data set was used in the simulation. The simulation environment in this research is the sim cloud environment. The comparison results show the improvement of waiting time and cost in the proposed approach. The results show that, on average, the proposed model has performed better by reducing the work time by 10% and increasing the use of resources by 16%.
[1] Huda, S. A., & Moh, S. (2022). Survey on computation offloading in UAV-Enabled mobile edge computing. Journal of Network and Computer Applications, 103341.
[2] Li, X., Lan, X., Mirzaei, A., & Bonab, M. J. A. (2022). Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing. Reliability Engineering & System Safety, 220, 108272.
[3] Sulieman, N. A., Ricciardi Celsi, L., Li, W., Zomaya, A., & Villari, M. (2022). Edge-Oriented Computing: A Survey on Research and Use Cases. Energies, 15(2), 452.
[4] Kumar, D., Baranwal, G., & Vidyarthi, D. P. (2022). A Survey on Auction based Approaches for Resource Allocation and Pricing in Emerging Edge Technologies. Journal of Grid Computing, 20(1), 1-52.
[5] Wang, Z., Lv, T., & Chang, Z. (2022). Computation offloading and resource allocation based on distributed deep learning and software defined mobile edge computing. Computer Networks, 108732.
[6] Qiu, H., Zhu, K., Luong, N. C., Yi, C., Niyato, D., & Kim, D. I. (2022). Applications of auction and mechanism design in edge computing: A survey. IEEE Transactions on Cognitive Communications and Networking.
[7] Qiu, H., Zhu, K., Luong, N. C., Yi, C., Niyato, D., & Kim, D. I. (2022). Applications of auction and mechanism design in edge computing: A survey. IEEE Transactions on Cognitive Communications and Networking.
[8] Li, X., Lan, X., Mirzaei, A., & Bonab, M. J. A. (2022). Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing. Reliability Engineering & System Safety, 220, 108272.
[9] Elgendy, I. A., Zhang, W., Tian, Y. C., & Li, K. (2019). Resource allocation and computation offloading with data security for mobile edge computing. Future Generation Computer Systems, 100, 531-541.
[10] Abbas, N.; Zhang, Y.; Taherkordi, A.; Skeie, T. Mobile Edge Computing: A Survey. IEEE Internet Things J. 2017, 5, 1–12.
[11] Wang, S.; Zhang, X.; Zhang, Y.; Wang, L.; Yang, J.; Wang, W. A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications. IEEE Access 2017, 5, 6757–6779.
[12] Sulieman, N. A., Ricciardi Celsi, L., Li, W., Zomaya, A., & Villari, M. (2022). Edge-Oriented Computing: A Survey on Research and Use Cases. Energies, 15(2), 452.
[13] Li, Y.; Wang, S. An energy-aware edge server placement algorithm in mobile edge computing. In Proceedings of the 2018 IEEE International Conference on Edge Computing (EDGE), San Francisco, CA, USA, 2–7 July 2018; pp. 66–73.
[14] Abbas, N.; Zhang, Y.; Taherkordi, A.; Skeie, T. Mobile edge computing: A survey. IEEE Internet Things J. 2017, 5, 450–465. [CrossRef]
[15] Maia, A.M.; Ghamri-Doudane, Y.; Vieira, D.; de Castro, M.F. Optimized placement of scalable iot services in edge computing. In Proceedings of the 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Washington, DC, USA, 8–12 April 2019; pp. 189–197.
[16] Xiao, K.; Gao, Z.; Wang, Q.; Yang, Y. A heuristic algorithm based on resource requirements forecasting for server placement in edge computing. In Proceedings of the 2018 IEEE/ACM Symposium on Edge Computing (SEC), Bellevue, WA, USA, 25–27 October 2018; pp. 354–355.
[17] Alrowaily, M.; Lu, Z. Secure edge computing in iot systems: Review and case studies. In Proceedings of the 2018 IEEE/ACM Symposium on Edge Computing (SEC), Bellevue, WA, USA, 25–27 October 2018; pp. 440–444.
[18] Li, X.; Ding, R.; Liu, X.; Yan, W.; Xu, J.; Gao, H.; Zheng, X. Comec: Computation offloading for video-based heart rate detection app in mobile edge computing. In Proceedings of the 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), Melbourne, Australia, 11–13 December 2018 ; pp. 1038–1039.
[19] Xing, H.; Liu, L.; Xu, J.; Nallanathan, A. Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing. IEEE Trans. Commun. 2019, 67, 4193–4207.
[20] Nowak, D.; Mahn, T.; Al-Shatri, H.; Schwartz, A.; Klein, A. A Generalized Nash Game for Mobile Edge Computation Offloading. In Proceedings of the 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (Mobile Cloud), Bamberg, Germany, 26–29 March 2018.
[21] Zhang, D., Piao, M., Zhang, T., Chen, C., & Zhu, H. (2020). New algorithm of multi-strategy channel allocation for edge computing. AEU-International Journal of Electronics and Communications, 126, 153372.
[22] Alfakih, T., Hassan, M. M., & Al-Razgan, M. (2021). Multi-objective accelerated particle swarm optimization with dynamic programing technique for resource allocation in mobile edge computing. IEEE Access, 9, 167503-167520..
[23] Ma, S., Song, S., Zhao, J., Zhai, L., & Yang, F. (2020). Joint network selection and service placement based on particle swarm optimization for multi-access edge computing. IEEE Access, 8, 160871-160881.