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Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

arXiv 1705.00704 ↗First posted 1 May 2017cs.NIcited by 14

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Abstract (arXiv)

Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this article, a MEC enabled multi-cell wireless network is considered where each Base Station (BS) is equipped with a MEC server that can assist mobile users in executing computation-intensive tasks via task offloading. The problem of Joint Task Offloading and Resource Allocation (JTORA) is studied in order to maximize the users' task offloading gains, which is measured by the reduction in task completion time and energy consumption. The considered problem is formulated as a Mixed Integer Non-linear Program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the NP-hardness of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, our approach is to decompose the original problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Numerical simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2017.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 5; 2025: 0; 2026: 1.

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  1. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  2. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approachcited by 17
  3. 2021A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networkscited by 7
  4. 2022Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computingcited by 4
  5. 2024Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencodercited by 4
  6. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  7. 2023Analysis and Optimization of Wireless Federated Learning with Data Heterogeneitycited by 3
  8. 2024Learning the Optimal Path and DNN Partition for Collaborative Edge Inferencecited by 3
  9. 2023Multi-objective Deep Reinforcement Learning for Mobile Edge Computingcited by 2
  10. 2023Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Responsecited by 2
  11. 2023A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloadingcited by 2
  12. 2022Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approachcited by 1
2 more
  1. 2024Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computingcited by 0
  2. 2026Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inferencecited by 0