Task Offloading and Scheduling Based on Mobile Edge Computing and Software-defined Networking

• Autorzy: Azeez Rawdhan Fatimah

Identyfikatory

DOI

10.26636/jtit.2025.1.1941

• Języki publikacji: EN

Abstrakty

EN

When integrated with mobile edge computing (MEC), software-defined networking (SDN) allows for efficient network management and resource allocation in modern computing environments. The primary challenge addressed in this paper is the optimization of task offloading and scheduling in SDN-MEC environments. The goal is to minimize the total cost of the system, which is a function of task completion lead time and energy consumption, while adhering to task deadline constraints. This multi-objective optimization problem requires balancing the trade-offs between local execution on mobile devices and offloading tasks to edge servers, considering factors such as computation requirements, data size, network conditions, and server capacities. This research focuses on evaluating the performance of particle swarm optimization (PSO) and Qlearning algorithms under full and partial offloading scenarios. Simulation-based comparisons of PSO and Q-learning show that for large data quantities, PSO is more cost efficient than the other algorithms, with the cost increase equaling approximately 0.001%per kilobyte, as opposed to 0.002%in the case of Q-learning. As far as energy consumption is concerned, PSO performs 84%and 23%better than Q-learning in the case of full and partial offloading, respectively. The cost of PSO is also less sensitive to network latency conditions than GA. Furthermore, the results demonstrate that Q-learning offers better scalability in terms of execution time as the number of tasks increases, and exceeds the outcomes achieved by PSO for task loads of more than 40. Such observations prove that PSO is better suited for large data transfers and energy-critical applications, whereas Q-learning is better suited for highly scalable environments and large numbers of tasks.

Słowa kluczowe

EN

energy efficiency; MEC; PSO; Q-learning; scalability; scheduling; SDN

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2025
• Tom: nr 1
• Strony: 30--37
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Azeez Rawdhan Fatimah

• Mustansiriyah University, Baghdad, Iraq

• https://orcid.org/0009-0006-8943-2759

Bibliografia

• [1] M. Satyanarayanan, “The Emergence of Edge Computing”, Computer, vol. 50, no. 1, pp. 30–39, 2017 (https://doi.org/10.1109/MC.2017.9).
• [2] Y. Mao et al., “A Survey on Mobile Edge Computing: The Communication Perspective”, IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017 (https://doi.org/10.1109/COMST.2017.2745201).
• [3] D. Kreutz et al., “Software-defined Networking: A Comprehensive Survey”, Proceedings of the IEEE, vol. 103, no. 1, pp. 14–76, 2015 (https://doi.org/10.1109/JPROC.2014.2371999).
• [4] Y. Wang et al., “Mobile-edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling”, IEEE Transactions on Communications, vol. 64, no. 10, pp. 4268–4282, 2016 (https: //doi.org/10.1109/TCOMM.2016.2599530).
• [5] H. Guo, J. Liu, and J. Zhang, “Computation Offloading for Multiaccess Mobile Edge Computing in Ultra-dense Networks”, IEEE Communications Magazine, vol. 56, no. 8, pp. 14–19, 2018 (https://doi.org/10.1109/MCOM.2018.1701069).
• [6] Y.Wei, F.R. Yu, M. Song, and Z. Han, “Joint Optimization of Caching, Computing, and Radio Resources for Fog-enabled IoT Using Natural Actor-critic Deep Reinforcement Learning”, IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2061–2073, 2019 (https://doi.org/10.1109/JIOT.2018.2878435).
• [7] L.Yin, J. Luo, and H. Luo, “Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing”,IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp.4712–4721, 2018 (https://doi.org/10.1109/TII.2018.2851241).
• [8] Y. Wang et al., “Cooperative Task Offloading in Three-tier Mobile Computing Networks: AnADMMFramework”, IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 2763–2776, 2019 (https://doi.org/10.1109/TVT.2019.2892176).
• [9] J. Li, H. Gao, T. Lv, and Y. Lu, “Deep Reinforcement Learning Based Computation Offloading and Resource Allocation for MEC”, IEEEWireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2022 (https://doi.org/10.1109/WCNC.2018.8377343).
• [10] G. Zhang et al., “Fair Task Offloading Among Fog Nodes in Fog Computing Networks”, IEEE International Conference on Communications (ICC), Kansas City, USA, 2018 (https://doi.org/10.1109/ICC.2018.8422316).
• [11] L. Tan, Z. Kuang, L. Zhao, and A. Liu, “Energy-Efficient JointTask Offloading and Resource Allocation in OFDMA-Based Collaborative Edge Computing”, in IEEE Transactions on WirelessCommunications, vol. 21, no. 3, pp. 1960–1972, 2022 (https://doi.org/10.1109/TWC.2021.3108641).
• [12] X. Chen et al., “Multi-tenant Cross-slice Resource Orchestration: A Deep Reinforcement Learning Approach”, IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2377–2392, 2022 (https://doi.org/10.1109/JSAC.2019.2933893).
• [13] J. Kim et al., “Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing”, IEEE INFOCOM 2020 – IEEE Conference on Computer Communications, Toronto, Canada, 2020 (https://doi.org/10.1109/INFOCOM41043.2020.9155510).
• [14] Y. Mao, J. Zhang, S.H. Song, and K.B. Letaief, “Stochastic Joint Radio and Computational Resource Management for Multi-user Mobile-edge Computing Systems”, IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 5994–6009, 2017 (https://doi.org/10.1109/TWC.2017.2717986).