Multi-access Edge Computing: A Survey

Tanaka, Hiroyuki; Yoshida, Masahiro; Möri, K.; Takahashi, Noriyuki

doi:10.2197/ipsjjip.26.87

Cited by 32 publications

(19 citation statements)

References 46 publications

(53 reference statements)

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“…MEC surveys have covered mechanisms for offloading compute-and data-intensive service provisioning from the end devices to the installed MEC server infrastructure; the offloading to other end devices has not been considered. In particular, the existing surveys have approached the MEC topic area from a variety of perspectives, including applications and use cases [16]- [20], opportunities and challenges [21], [22], security threats and mechanisms [23]- [25], computation offloading [26]- [30], caching [31], [32], communication perspective [33], [34], service migration [35], architecture and orchestration [36]- [40], technological developments [41], and edge computing for IoT [42]- [45]. Closely related to the MEC surveys are surveys on fog computing.…”

Section: B Related Surveys On Mec and D2d Communicationmentioning

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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Section: B Related Surveys On Mec and D2d Communicationmentioning

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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“…High-resolution video processing and Content Delivery Networks (CDNs) can greatly benefit from MEC and NFV. References [5,[13][14][15][16][17][18][19][20][21][22] summarize some relevant results in this field. In particular, Reference [13] shows the improvement in QoE perceived by users when CDNs are extended to the edge through the use of NFV and cloud technologies.…”

Section: The Immersive Video Use Casementioning

confidence: 99%

“…Reference [5] provides a state-of-the-art survey on research efforts in MEC, pointing out challenges, open issues, and opportunities. In this regard, Reference [19] defines the differences between an MEC and a Cloud Radio Access Network (RAN) approach, demonstrating that they are not exclusive technologies; rather, they complete each other and can work together in a heterogeneous mix.…”

Section: The Immersive Video Use Casementioning

confidence: 99%

“…Edge Computing makes it possible to minimize transmission delay and achieve high data rates locally. Furthermore, applications running at the edge can exploit specific real-time information provided by the Radio Access Network, such as radio link status or the geographic location of consumers [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the works available concerning MEC focus on traditional video networking functionalities, such as video caching or video streaming [5,[13][14][15][16][17][18][19][20][21][22][23][24]. Conversely, the problem of successfully combining end-user apps and ME apps to provide interactive and immersive video services to consumers on a peer-to-peer basis is a topic that has received little attention from researchers so far.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case

Cattaneo

Giust²,

Meani

et al. 2018

Computers

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Multi-access Edge Computing (MEC) will be a technology pillar of forthcoming 5G networks. Nonetheless, there is a great interest in also deploying MEC solutions in current 4G infrastructures. MEC enables data processing in proximity to end users. Thus, latency can be minimized, high data rates locally achieved, and real-time information about radio link status or consumer geographical position exploited to develop high-value services. To consolidate network elements and edge applications on the same virtualization infrastructure, network operators aim to combine MEC with Network Function Virtualization (NFV). However, MEC in NFV integration is not fully established yet: in fact, various architectural issues are currently open, even at standardization level. This paper describes a novel MEC in an NFV system which successfully combines, at management level, MEC functional blocks with an NFV Orchestrator, and can neutrally support any “over the top” Mobile Edge application with minimal integration effort. A specific ME app combined with an end-user app for the provision of immersive video services is presented. To provide low latency, CPU-intensive services to end users, the proposed architecture exploits High-Performance Computing resources embedded in the edge infrastructure. Experimental results showing the effectiveness of the proposed architecture are reported and discussed.

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Deep reinforcement learning‐based resource allocation in multi‐access edge computing

Khani,

Sadr,

Jamali

2023

Concurrency and Computation

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SummaryNetwork architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC.

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Multi-access Edge Computing: A Survey

Cited by 32 publications

References 46 publications

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

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

Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case

Deep reinforcement learning‐based resource allocation in multi‐access edge computing

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