CDS-MEC: NFV/SDN-based Application Management for MEC in 5G Systems

Schiller, Eryk; Nikaein, Navid; Kalogeiton, Eirini; Gasparyan, Mikael; Braun, Torsten

doi:10.1016/j.comnet.2018.02.013

Cited by 40 publications

(16 citation statements)

References 28 publications

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“…Mobile Edge Computing (MEC) emerged as an essential technology implementation for 4G and can be quickly adopted for the 5G network. It intelligently merges network conditions, location, and radio information to serve users efficiently [125]. In both 4G MEC and 5G MEC, consumers can decide the place of MEC installations.…”

Section: ) Mecmentioning

confidence: 99%

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

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Section: ) Mecmentioning

confidence: 99%

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

et al. 2020

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“…Logically centralized SDN controllers help to solve classical networking problems, such as routing, tunneling, and IP address translation, as well as new challenges in future 5G applications, such as UE mobility, adaptation to service degradation, as well as security and integrated protection for IoT systems [44], [95]. Through SDN, network traffic flows can be flexibly steered to and from the MEC [96], [97] so as to seamlessly integrate MEC computations and caching into the provisioning of network services to mobile applications.…”

Section: ) Software Defined Networking (Sdn)mentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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“…The selection of the optimal network edge node results in a decrease in the end-to-end latency. Schiller et al [150] develop an NFV/EDGE/SDN platform that uses VNFs to flexibly manage EDGE applications and improve the user QoE (e.g., latency and throughput). Yang et al [151] propose a solution to take advantage of the low latency benefit of edge computing without wasting resources during stable/low-workload periods of the fixed-location traditional solution.…”

Section: ) Edge Computing (Edge)mentioning

confidence: 99%

“…Finally, Cziva et al [152] combine edge computing with virtualization, deploying VNFs (virtual network functions) in different scenarios. Their results show Garcia et al [142], [143] Ctxt Awa: Cross-layer Slice Mgmt: Local Breakout Zhang et al [144] Slice Mgmt: Offloading CASH [145] Ctxt Awa: Application Ctxt Awa: Network Lee et al [146] Slice Mgmt: Local Breakout Cattaneo et al [147] Slice Mgmt: Local Breakout Huang et al [148] Ctxt Awa: Network Ctxt Awa: Cross-layer Heinonen et al [149] Slice Mgmt: EDGE Selection Schiller et al [150] DP Mgmt: Caching Yang et al [151] VNF MANO: Scaling Cziva et al [152] VNF MANO: Plcmt & Migr. Nunna et al [153] Ctxt Awa: Network Dutta et al [154] Slice Mgmt: Offloading…”

Section: ) Edge Computing (Edge)mentioning

confidence: 99%

“…These approaches are much more complex than the SON methods used in the RAN segment in 3G and 4G and imply a hard modelling of the network to represent the reference behaviour KPIs. Some interesting contributions that are leading the research in this direction are Balevi et al [209], Morocho-Cayamcela et al [210] or the aforementioned work of Eurecom [150].…”

Section: ) 5g End-to-end Self-organizing Networkmentioning

confidence: 99%

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A Survey of End-to-End Solutions for Reliable Low-Latency Communications in 5G Networks

Rico

Merino

2020

IEEE Access

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Very low latency and high reliability are two of the main requirements for new applications exploiting 5G networks. This is the case for the remote operation of robots or vehicles, the autonomous interaction of equipment in a factory, autonomous driving and tactile internet applications. Although the TCP/IP stack has been sufficient as the end-to-end solution for most of the history of the Internet, a number of surveys have appeared recently presenting many different methods for managing the end-to-end communication to meet the requirements of various technologies such as that of 5G networks. In this paper, we present a novel classification of the literature focused on new end-to-end solutions and the creation of services towards the support of low latency (1 ms) and high reliability (10-9 error rate) in current and future 5G networks. We specifically highlight how the proposals can be classified according to enabling technologies and the specific method used to achieve success in terms of the latency and reliability. The literature related to end-to-end solutions for reliable low-latency communications are organized according to three main topics: (i) end-to-end protocols that improve communication in terms of latency and reliability, (ii) functionality or technologies implemented on the network to support the current demands, and (iii) application programming interfaces that enhance the correct utilization of those protocols and additional technologies.

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CDS-MEC: NFV/SDN-based Application Management for MEC in 5G Systems

Cited by 40 publications

References 28 publications

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

A Survey of End-to-End Solutions for Reliable Low-Latency Communications in 5G Networks

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