Edge Computing and Networking: A Survey on Infrastructures and Applications

Zhao, Yongli; Wang, Wei; Li, Yajie; Colman-Meixner, Carlos; Tornatore, Massimo; Zhang, Jie

doi:10.1109/access.2019.2927538

Cited by 69 publications

(36 citation statements)

References 88 publications

(83 reference statements)

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“…MEC will also play a fundamental role in 5G to meet low-latency service requirements [9,20]. Recently, different architectures have been proposed for edge computing [21]. Edge data centers can be co-located with 4G or 5G baseband processing functions to reduce the delay by bringing services closer to the users [9].…”

Section: Introductionmentioning

confidence: 99%

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

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The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

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Section: Introductionmentioning

confidence: 99%

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

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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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“…The edge computing brings the location of data storage and processing, near to the end device, instead of sending the produced data of the end devices to the remote data centers . In edge computing, the data are sent through the local gateways to the MEC servers to be processed or preprocessed . If the data processing is finished, then the processing results are sent to the user.…”

Section: Related Workmentioning

confidence: 99%

“…6,7 In edge computing, the data are sent through the local gateways to the MEC servers to be processed or preprocessed. 8,9 If the data processing is finished, then the processing results are sent to the user. Else, the remaining processing is offloaded to the cloud servers.…”

Section: Mobile Edge Computingmentioning

confidence: 99%

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Abdoos

Taheri

Taeezadeh

2019

Trans Emerging Tel Tech

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We propose a computation that compensates the weakness of the processing power and storage capacity of the dew and edge servers. In the proposed computation, the nodes near to each other, which can be the dew, edge, or cloud servers, form a computational grid. The dew servers can be mobile devices, laptops, or computers. Their processing power and storage capacity can be used when they are idle. Our goal is to achieve load balancing of different nodes (servers). A server of the grid with proper processing power, storage capacity, remained energy, and low task load is selected as the grid head (each server of grid shares its specification with the other servers of grid periodically). The grid head (server) is responsible for grid communications. The grid head shares the specification of the grid, with the neighbor nodes (we suppose that the end device is aware of the specification of the nearest dew, edge, and cloud grid heads to itself). The end device decides about sending its created tasks to the grid head of a dew or edge or cloud grid, with attention to the distance between itself and the grid head, and the free processing power and storage capacity of the grid. The scheduling algorithm of a grid is the combination of first‐come–first‐served and priority scheduling algorithms. The simulation results show that the proposed computation has less delay and energy consumption and more efficiency than the cloud and edge computations.

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Edge Computing and Networking: A Survey on Infrastructures and Applications

Cited by 69 publications

References 88 publications

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

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

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

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