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 toward 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 discuss a set of issues, challenges, and future research 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.

A Survey on Mobile Edge Computing: The Communication Perspective

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.

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

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On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

A Survey on the Edge Computing for the Internet of Things

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Deep Learning in Mobile and Wireless Networking: A Survey

Mobile-Edge computing (MEC) is an emerging technology currently recognized as a key enabler for 5G networks. Compatible with current 4G networks, MEC will address many key uses of the 5G system, motivated by the massive diffusion of the Internet of Things (IoT). This article aims to provide a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the European Telecommunications Standards Institute (ETSI) MEC Industry Specification Group (ISG) standardization organization. We provide some examples of MEC deployment, with special reference to IoT cases, since IoT is recognized as a main driver for 5G. Finally, we discuss the main benefits and challenges for MEC moving toward 5G.

Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things.

The ever-increasing demand of mobile Internet traffic is pushing operators to look for solutions to increase the available bandwidth per user and per unit of area. At the same time, they need to reduce the load in the core network at a reasonable cost in their future 5G deployments. Today’s trend points to the deployment of extremely dense networks in order to provide ubiquitous connectivity at high data rates. However, this is hard to couple with the current mobile networks’ architecture, which is heavily centralized, posing difficult challenges when coping with the foreseen explosion of mobile data. Additionally, future 5G networks will exhibit disparate types of services, posing different connectivity requirements. Distributed mobility management is emerging as a valid framework to design future mobile network architectures, taking into account the requirements for large traffic in the core and the rise of extremely dense wireless access networks. In this article, we discuss the adoption of a distributed mobility management approach for mobile networks, and analyze the operation of the main existing solutions proposed so far, including a first practical evaluation based on experiments with real Linux-based prototype implementations.

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Distributed mobility management for future 5G networks: overview and analysis of existing approaches

The number of mobile users and their traffic demand is expected to be
ever-increasing in future years, and this growth can represent a
limitation for deploying current mobility management schemes that are
intrinsically centralized, e.g., Mobile IPv6 and Proxy Mobile IPv6.
For this reason it has been waved a need for distributed and dynamic
mobility management approaches, with the objective of reducing
operators' burdens, evolving to a cheaper and more efficient
architecture. This draft describes multiple solutions for network-
based distributed mobility management inspired by the well known Proxy
Mobile IPv6.

A PMIPv6-based solution for Distributed Mobility Management

Mobile Internet traffic is growing steeply, mainly due to the deployment of new broadband wireless technologies and the ever increasing connectivity demand coming from new services being available to mobile users. Current mobile network architectures rely on centralized mobility protocols which intrinsically pose enormous burdens on the central anchors, both in terms of connectivity needs and user mobility management. In order to face these issues, a new paradigm, called Distributed Mobility Management, is being explored, based on flattening the network architecture by deploying multiple mobility anchors at the edge of the network. In this article we conduct an analytic and experimental evaluation of a network-based IP distributed mobility management solution that leverages Proxy Mobile IPv6 protocol operations. We develop an analytic model of the signaling and packet delivery costs, as well as the handover latency of both Proxy Mobile IPv6 and our distributed solution. We have also implemented a Linux-based prototype of our proposal, which has been used to experimentally assess the handover latency in a real IEEE 802.11 scenario. Finally, we use the results obtained from the analytic and experimental performance to evaluate the benefits that could be achieved by deploying a distributed mobility management solution.

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Analytic Evaluation and Experimental Validation of a Network-Based IPv6 Distributed Mobility Management Solution

The automotive and telco industries have taken an investment bet on the connected car market, pushing for the digital transformation of the sector by exploiting recent information and communications technology (ICT) progress. As ICT developments continue, it is expected that the technology advancements will be able to fulfill the sophisticated requirements for vehicular use cases, such as: low latency and reliable communications for safety; increased computing power to process large amounts of sensed data; and increased bandwidth for onboard infotainment. The aforementioned requirements have received significant focus during the ongoing definition of the 3GPP 5G mobile standards, where there has been a drive to facilitate vertical segments such as automotive, in addition to providing the core aspects of the communication infrastructure. Among the technology enablers for 5G, multi-access edge computing (MEC) can be considered essential. That is, a cloud environment located at the edge of the network, in proximity of the end users and coupled with the service provider's network infrastructure. Even before 5G is rolled out, current mobile networks can already target support for these challenging use cases using MEC technology. This is because MEC is able to fulfill low latency and high bandwidth requirements, and, in addition, lends itself to be deployed at vertical industrial sector premises such as road infrastructure, air/seaports, and smart factories, thus bringing computing power where it is needed most. This work showcases the automotive use cases that are relevant for MEC, providing insights into the technologies specified and investigated by the ETSI Industry Specification Group MEC, who were the pioneers in creating a standardized computing platform for advanced mobile networks with regard to network edge related use cases.

Fabio Giust

Papers

Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things.

Distributed mobility management for future 5G networks: overview and analysis of existing approaches

A PMIPv6-based solution for Distributed Mobility Management

Analytic Evaluation and Experimental Validation of a Network-Based IPv6 Distributed Mobility Management Solution

Multi-Access Edge Computing: The Driver Behind the Wheel of 5G-Connected Cars