[Paper Review] Mobile Edge Computing: Survey and Research Outlook.

Yuyi Mao, Changsheng You+3 more|arXiv (Cornell University)|Jan 4, 2017

IoT and Edge/Fog Computing143 references174 citations

TL;DR

This paper surveys Mobile Edge Computing (MEC) as a paradigm shift from centralized cloud computing to network edges, enabling low-latency, energy-efficient execution of computation-intensive applications on mobile devices. It focuses on joint radio-and-computational resource management and outlines key research directions including mobility, caching, green operations, privacy, and standardization.

ABSTRACT

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Motivation & Objective

• To address the limitations of centralized Mobile Cloud Computing by shifting computation, control, and storage to network edges such as base stations and access points.
• To reduce latency and mobile energy consumption for computation-intensive and latency-critical applications on resource-constrained devices.
• To bridge wireless communications and mobile computing through integrated system designs for efficient resource management.
• To identify and outline promising research directions for advancing MEC from theory to practical deployment.
• To summarize recent standardization efforts and illustrate real-world MEC application scenarios.

Proposed method

• Conducting a comprehensive survey of state-of-the-art MEC research with emphasis on joint radio-and-computational resource management techniques.
• Analyzing system architectures that integrate mobile computing and wireless networking at the network edge.
• Proposing a framework for MEC system deployment based on edge node placement and service function chaining.
• Integrating cache mechanisms into MEC to reduce backhaul load and improve service response time.
• Addressing mobility management through predictive offloading and handover optimization at edge nodes.
• Introducing energy efficiency models and green MEC strategies to minimize operational power consumption.

Experimental results

Research questions

• RQ1 How can radio and computational resources be jointly optimized in MEC to minimize latency and energy consumption?
• RQ2 What are the key challenges in deploying MEC systems at scale, and how can they be addressed through system design?
• RQ3 How can caching at the edge improve the performance and scalability of MEC networks?
• RQ4 What mechanisms are needed to support seamless mobility and handover in mobile MEC environments?
• RQ5 How can MEC be made energy-efficient and sustainable through green computing techniques?

Key findings

• MEC significantly reduces end-to-end latency and mobile device energy consumption by offloading computation to nearby network edges.
• Joint radio-and-computational resource management is essential for achieving optimal performance in MEC systems.
• Cache-enabled MEC reduces backhaul traffic and improves service delivery speed for frequently accessed data.
• Mobility-aware MEC designs improve service continuity and reduce handover latency in high-speed scenarios.
• Green MEC strategies, including dynamic resource allocation and sleep modes, can substantially reduce energy consumption.
• Standardization efforts are actively progressing, with key frameworks being developed by 3GPP and ETSI to enable interoperable MEC deployments.