# Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

arXiv: https://arxiv.org/abs/1510.00888 · HTML: https://mlsanity.com/paper/1510.00888/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2015.

## Built on by (most cited first, 28 of 28)

- [Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning](https://mlsanity.com/paper/2205.11854/) (2022)
- [Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT](https://mlsanity.com/paper/2110.14937/) (2021)
- [Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2209.13425/) (2022)
- [Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks](https://mlsanity.com/paper/2411.06773/) (2024)
- [Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach](https://mlsanity.com/paper/2112.11818/) (2021)
- [Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions](https://mlsanity.com/paper/2204.02267/) (2022)
- [Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System](https://mlsanity.com/paper/2101.06582/) (2021)
- [QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles](https://mlsanity.com/paper/2202.01696/) (2022)
- [Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators](https://mlsanity.com/paper/2306.05041/) (2023)
- [Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices](https://mlsanity.com/paper/2501.15305/) (2025)
- [MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networks](https://mlsanity.com/paper/2203.07999/) (2022)
- [Distributed Task Management in Fog Computing: A Socially Concave Bandit Game](https://mlsanity.com/paper/2203.14572/) (2022)
- [Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing](https://mlsanity.com/paper/2306.06022/) (2023)
- [DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing](https://mlsanity.com/paper/2110.01863/) (2021)
- [Monitoring and Proactive Management of QoS Levels in Pervasive Applications](https://mlsanity.com/paper/2206.05478/) (2022)
- [Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application](https://mlsanity.com/paper/2208.04237/) (2022)
- [FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations](https://mlsanity.com/paper/2209.14399/) (2022)
- [Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control](https://mlsanity.com/paper/2212.13141/) (2022)
- [Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing](https://mlsanity.com/paper/2310.01443/) (2023)
- [DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem](https://mlsanity.com/paper/2408.05712/) (2024)
- [Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks](https://mlsanity.com/paper/2409.07911/) (2024)
- [MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems](https://mlsanity.com/paper/2508.17341/) (2025)
- [Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks](https://mlsanity.com/paper/2509.10163/) (2025)
- [Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing](https://mlsanity.com/paper/2510.14287/) (2025)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)
- [Evaluating Device-First Continuum AI (DFC-AI) for Autonomous Operations in the Energy Sector](https://mlsanity.com/paper/2511.17528/) (2025)
- [TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MEC](https://mlsanity.com/paper/2601.06191/) (2026)
- [Entropy-Aware Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2603.16949/) (2026)

## Abstract (arXiv)

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.