International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 3, 8 Jun. 2026
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Q-Learning-Based Task Scheduling for Low-Latency Edge Offloading in MEC Systems
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Author(s)
B. Swapna
1,*
K. Ravindranath
1
Index Terms
Mobile Edge Computing (MEC), Task scheduling, Task offloading, Q learning, Optimization
Abstract
Mobile Edge Computing (MEC) handles energy constraints and enhances performance by facilitating the effective offloading of applications that are delay-sensitive and computationally demanding from mobile devices. Nevertheless, high computing complexity, network limits, and the possibility of task failures brought on by user mobility and resource constraints make efficient task scheduling difficult. To address the limitations, the Q-Optimize OffloadPro Framework (QOOPF) is proposed as a task scheduling and offloading system designed to manage high virtual machine utilization, reduce latency, and improve resource efficiency in MEC. The framework incorporates the OffloadPro Scheduling Method (OPSM), which optimizes task assignment by prioritizing tasks based on a critical path approach to ensure effective offloading. To ensure that task offloading choices in edge computing settings are made dynamically, this technique is augmented by a Deep-Q-Driven Policy-Value Optimizer that has been trained on large amounts of task data. QOOPF dynamically balances computational loads, reduces task failures, and increases resource consumption by combining Policy Value Optimization (PVO) with Q learning. The experimental findings demonstrate QOOPF achieves a makespan of 720 seconds and variance of 30.03 for 300 tasks, with VM results showing a makespan of 445.88 seconds and variance of 4.58 for 16 VMs, scaling efficiently with up to 608.54 seconds and 6.08 variance for 32 VMs for high-demand MEC situations. This method provides an efficient, scalable solution for dynamic computing requirements while successfully addressing scheduling constraints.
Cite This Paper
B. Swapna, K. Ravindranath, "Q-Learning-Based Task Scheduling for Low-Latency Edge Offloading in MEC Systems", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.3, pp. 158-183, 2026. DOI:10.5815/ijcnis.2026.03.09
Reference
[1]M. Tang, and V. W. Wong, “Deep reinforcement learning for task offloading in mobile edge computing systems,” IEEE Transactions on Mobile Computing, vol. 21, no. 6, pp. 1985-1997, 2020
[2]U. Saleem, Y. Liu, S. Jangsher, Y. Li, and T. Jiang, “Mobility-aware joint task scheduling and resource allocation for cooperative mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 360-374, 2020.
[3]H. Hu, W. Song, Q. Wang, R. Q. Hu, and H. Zhu, “Energy efficiency and delay tradeoff in an MEC-enabled mobile IoT network,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 15942-15956, 2022.
[4]K. Sadatdiynov, L. Cui, L. Zhang, J. Z. Huang, S. Salloum, and M. S. Mahmud, “A review of optimization methods for computation offloading in edge computing networks,” Digital Communications and Networks, vol. 9, no. 2, pp. 450-461, 2023.
[5]Z. Kuang, Z. Ma, Z. Li, and X. Deng, “Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing,” Journal of Systems Architecture, vol. 118, pp. 102167.2021.
[6]Y. Ma, W. Liang, M. Huang, W. Xu, and S. Guo, “Virtual network function service provisioning in MEC via trading off the usages between computing and communication resources,” IEEE Transactions on Cloud Computing, vol. 10, no. 4, pp. 2949-2963, 2020
[7]J. Wang, J. Hu, G. Min, W. Zhan, A. Y. Zomaya, and N. Georgalas, “Dependent task offloading for edge computing based on deep reinforcement learning,” IEEE Transactions on Computers, vol. 71, no. 10, pp. 2449-2461, 2021
[8]Raeisi-Varzaneh, M., Dakkak, O., Habbal, A., & Kim, B. S. (2023). Resource scheduling in edge computing: Architecture, taxonomy, open issues and future research directions. IEEE Access, 11, 25329-25350.
[9]Q. Luo, S. Hu, C. Li, G. Li, and W. Shi, “Resource scheduling in edge computing: A survey,” IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2131-2165, 2021
[10]S. K. U. Zaman, A. I. Jehangiri, T. Maqsood, Z. Ahmad, A. I. Umar, J. Shuja, and W. Alasmary, “Mobility-aware computational offloading in mobile edge networks: a survey,” Cluster Computing, pp. 1-22, 2021.
[11]P. Wei, K. Guo, Y. Li, J. Wang, W. Feng, S. Jin, and Y. C. Liang, “Reinforcement learning-empowered mobile edge computing for 6G edge intelligence,” IEEE Access, vol. 10, pp. 65156-65192, 2022.
[12]I. Khan, X. Tao, G. S. Rahman, W. U. Rehman, and T. Salam, “Advanced energy-efficient computation offloading using deep reinforcement learning in MTC edge computing,” IEEE access, vol. 8, pp. 82867-82875, 2020.
[13]W. Zhan, C. Luo, G. Min, C. Wang, Q. Zhu, and H. Duan, “Mobility-aware multi-user offloading optimization for mobile edge computing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 3, pp. 3341-3356, 2020.
[14]E. F. Maleki, L. Mashayekhy, and S. M. Nabavinejad, “Mobility-aware computation offloading in edge computing using machine learning,”. IEEE Transactions on Mobile Computing, vol. 22, no. 1, pp. 328-340, 2021
[15]A. Shakarami, A. Shahidinejad, and M. Ghobaei-Arani, “An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach,” Journal of Network and Computer Applications, vol. 178, pp. 102974, 2021.
[16]Z. Chen, J. Hu, X. Chen, J. Hu, X. Zheng, and G. Min, “Computation offloading and task scheduling for DNN-based applications in cloud-edge computing,” IEEE Access, vol. 8, pp. 115537-115547, 2020.
[17]Y. Miao, G. Wu, M. Li, A. Ghoneim, M. Al-Rakhami, and M. S. Hossain, “Intelligent task prediction and computation offloading based on mobile-edge cloud computing,” Future Generation Computer Systems, vol. 102, pp. 925-931, 2021
[18]L. Ale, N. Zhang, X. Fang, X. Chen, S. Wu, and L. Li, “Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 3, pp. 881-892.
[19]G. Qu, H. Wu, R. Li, and P. Jiao, “DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing,” IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3448-3459, 2021.
[20]Z. Chen, J. Hu, G. Min, C. Luo, and El- T. Ghazawi, “Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning,”. IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 8, pp. 1911-1923, 2021
[21]Z. Tang, W. Jia, X. Zhou, W. Yang, and Y. You, “Representation and reinforcement learning for task scheduling in edge computing, IEEE Transactions on Big Data, vol. 8, no. 3, pp. 795-808, 2021.
[22]A. Robles-Enciso, and A. F. Skarmeta, “A multi-layer guided reinforcement learning-based tasks offloading in edge computing,” Computer Networks, vol. 220, pp. 109476, 2023
[23]T. Zheng, J. Wan, J. Zhang, and C. Jiang, “Deep reinforcement learning-based workload scheduling for edge computing,” Journal of Cloud Computing, vol. 11, no. 1, pp. 3, 2022.
[24]B. Sellami, A. Hakiri, S. B. Yahia, and P. Berthou, “Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network,” Computer Networks, vol. 210, pp. 108957.2022
[25]L. Jin, M. Tang, M. Zhang, and H. Wang, “Fractional deep reinforcement learning for age-minimal mobile edge computing,” In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, no. 11, pp. 12947-12955,2024.
[26]L. Zeng, Q. Liu, S. Shen, and X. Liu, “Improved double deep Q network-based task scheduling algorithm in edge computing for Makespan optimization,” Tsinghua Science and Technology, vol. 29, no. 3, pp. 806-817, 2023.
[27]W. Qi, RETRACTED: Optimization of cloud computing task execution time and user QoS utility by improved particle swarm optimization, 2021.
[28]P. Gazori, D. Rahbari, and M. Nickray, “Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach,” Future Generation Computer Systems, vol. 110, pp. 1098-1115, 2020.
[29]Q. Liu, H. Zhang, X. Zhang, and D. Yuan, “Joint service caching, communication and computing resource allocation in collaborative MEC systems: A DRL-based two-timescale approach,” IEEE Transactions on Wireless Communications, 2024.
[30]J. Anand, and B. Karthikeyan, “EADRL: Efficiency-Aware Adaptive Deep Reinforcement Learning for Dynamic Task Scheduling in Edge-Cloud Environments,” Results in Engineering, pp. 105890, 2025.
[31]M. N. Tariq, J. Wang, S. Raza, M. Siraj, M. Altamimi, and S. Memon, “Toward Optimal Resource Allocation: A Multi-Agent DRL Based Task Offloading Approach in Multi-UAV-Assisted MEC Networks,” IEEE Access, vol. 12, pp. 81428-81440, 2024.
[32]T. Du, C. Li, and Y. Luo, “Latency-aware computation offloading and DQN-based resource allocation approaches in SDN-enabled MEC,” Ad Hoc Networks, vol. 135, pp. 102950, 2022.