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

arXiv: https://arxiv.org/abs/1705.00704 · HTML: https://mlsanity.com/paper/1705.00704/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.06986/) (2024)
- [A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks](https://mlsanity.com/paper/2104.09382/) (2021)
- [Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing](https://mlsanity.com/paper/2210.13464/) (2022)
- [Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder](https://mlsanity.com/paper/2401.12167/) (2024)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity](https://mlsanity.com/paper/2308.03521/) (2023)
- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)
- [Multi-objective Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2307.14346/) (2023)
- [Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response](https://mlsanity.com/paper/2308.11088/) (2023)
- [A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading](https://mlsanity.com/paper/2309.00907/) (2023)
- [Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2206.05881/) (2022)
- [Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing](https://mlsanity.com/paper/2403.08687/) (2024)
- [Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference](https://mlsanity.com/paper/2608.13863/) (2026)

## 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.