Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Abstract

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.

28 pages, 21 figures, Accepted for publication in IEEE Communications Surveys and Tutorials

References in corpus (2)

• Mobility-Induced Service Migration in Mobile Micro-Clouds
• CONCERT: A Cloud-Based Architecture for Next-generation Cellular Systems

Cited by in corpus (17)

• Delay-aware and Energy-Efficient Computation Offloading in Mobile Edge Computing Using Deep Reinforcement Learning
• Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles
• A Review on Computational Intelligence Techniques in Cloud and Edge Computing
• Heterogeneous Task Offloading and Resource Allocations via Deep Recurrent Reinforcement Learning in Partial Observable Multi-Fog Networks
• A Decentralized Framework for Serverless Edge Computing in the Internet of Things
• Security modeling and efficient computation offloading for service workflow in mobile edge computing
• Aerial Computing: A New Computing Paradigm, Applications, and Challenges
• EcoMobiFog -- Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications
• Noncoherent Joint Transmission Beamforming for Dense Small Cell Networks: Global Optimality, Efficient Solution and Distributed Implementation
• 5G Enabled Fault Detection and Diagnostics: How Do We Achieve Efficiency?
• Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach
• Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset
• An Application-Driven Non-Orthogonal Multiple Access Enabled Computation Offloading Scheme
• PECCO: A Profit and Cost-oriented Computation Offloading Scheme in Edge-Cloud Environment with Improved Moth-flame Optimisation
• Cloud Deployment Tradeoffs for the Analysis of Spatially-Distributed Systems of Internet-of-Things
• Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application
• Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior