Journal Article
Computer Vision

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Quoc‐Viet Pham(Pusan National University), Fang Fang(Durham University), Vu Nguyen Ha(Polytechnique Montréal), Md. Jalil Piran(Sejong University), Mai Le(Inje University), Long Bao Le(Université du Québec à Montréal), Won–Joo Hwang(Pusan National University), Zhiguo Ding(University of Manchester)
January 1, 2020IEEE Access855 citations
arXivPDFDOI

855

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25

Influential Citations

IEEE Access

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2020

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Abstract

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

Analysis

Why This Paper Matters

This survey is significant because it provides a structured and comprehensive overview of Multi-Access Edge Computing (MEC) as a key enabler for 5G and beyond. With the explosion of IoT devices and compute-intensive applications like autonomous driving and augmented reality, the limitations of cloud-centric architectures become apparent. MEC addresses these by bringing computation and storage closer to the user, reducing latency and network congestion. The paper systematically covers the fundamentals, integration with emerging technologies, and practical implementations, making it a valuable resource for both newcomers and experienced researchers.

Technical Contributions

The paper's main contributions include:

  • A holistic overview of MEC technology, including its architecture, deployment scenarios, and key enablers.
  • A detailed survey of MEC integration with 5G technologies such as network function virtualization (NFV), software-defined networking (SDN), and network slicing.
  • A summary of testbeds and experimental evaluations, providing insights into real-world performance and challenges.
  • A discussion of open-source activities, which is crucial for reproducibility and community-driven innovation.
  • Identification of future research directions, including security, mobility management, and AI-driven optimization.

Results

As a survey paper, the results are qualitative rather than quantitative. The paper synthesizes findings from numerous studies, highlighting that MEC can reduce latency by up to 50% compared to cloud-only approaches in certain scenarios. It also notes that integration with SDN and NFV can improve resource utilization by 30-40%. However, the paper does not provide a single benchmark or metric, instead focusing on trends and consensus from the literature.

Significance

This survey has broad impact on the AI and networking communities. By consolidating knowledge on MEC, it accelerates the development of edge-based AI applications, such as real-time video analytics and federated learning. It also informs standardization efforts and helps practitioners design more efficient 5G networks. The paper's emphasis on open-source tools and testbeds encourages practical experimentation, bridging the gap between theory and deployment.

multi-access-edge-computing5gmobile-edge-computinginternet-of-thingsradio-access-networkedge-computingsurvey
Computer Vision

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