# Mobile Edge Computing: A Survey on Architecture and Computation Offloading

arXiv: https://arxiv.org/abs/1702.05309 · HTML: https://mlsanity.com/paper/1702.05309/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2017.

## Built on by (most cited first, 30 of 53)

- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Communication-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2110.15318/) (2021)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT](https://mlsanity.com/paper/2202.07391/) (2022)
- [Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.02616/) (2024)
- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [FedGiA: An Efficient Hybrid Algorithm for Federated Learning](https://mlsanity.com/paper/2205.01438/) (2022)
- [Character-level HyperNetworks for Hate Speech Detection](https://mlsanity.com/paper/2111.06336/) (2021)
- [GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks](https://mlsanity.com/paper/2412.08296/) (2024)
- [Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2312.00279/) (2023)
- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions](https://mlsanity.com/paper/2204.02267/) (2022)
- [The Case for Hierarchical Deep Learning Inference at the Network Edge](https://mlsanity.com/paper/2304.11763/) (2023)
- [Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint](https://mlsanity.com/paper/2112.11413/) (2021)
- [Edge-centric Optimization of Multi-modal ML-driven eHealth Applications](https://mlsanity.com/paper/2208.02597/) (2022)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model](https://mlsanity.com/paper/2412.20820/) (2024)
- [Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning](https://mlsanity.com/paper/2102.02078/) (2021)
- [Federated Ensemble Model-based Reinforcement Learning in Edge Computing](https://mlsanity.com/paper/2109.05549/) (2021)
- [Heterogeneous Federated Learning on a Graph](https://mlsanity.com/paper/2209.08737/) (2022)
- [Multi-objective Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2307.14346/) (2023)
- [Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance](https://mlsanity.com/paper/2504.11197/) (2025)
- [On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2205.09925/) (2022)
- [On the Regret of Online Edge Service Hosting](https://mlsanity.com/paper/2303.06851/) (2023)
- [Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing](https://mlsanity.com/paper/2306.06022/) (2023)
- [Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures](https://mlsanity.com/paper/2502.06963/) (2025)
- [Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks](https://mlsanity.com/paper/2504.03052/) (2025)
- [Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2604.07148/) (2026)
- [Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry](https://mlsanity.com/paper/2609.26068/) (2026)

## Abstract (arXiv)

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.