Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

Liu, Yaqiong; Peng, Mugen; Shou, Guochu; Chen, Yudong; Chen, Siyu

doi:10.1109/jiot.2020.3004500

Cited by 347 publications

(101 citation statements)

References 166 publications

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“…There are three common edge computing models: Cloudlets [9], fog computing [10], and multiaccess/mobile edge computing [11]. MEC, as a new architecture that moves service capabilities from the core network to the edge network, has caused extensive research by scholars [6,[12][13][14].…”

Section: Related Workmentioning

confidence: 99%

Joint optimization of network selection and task offloading for vehicular edge computing

Tang

Zhang

et al. 2021

J Cloud Comp

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Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

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Section: Related Workmentioning

confidence: 99%

Joint optimization of network selection and task offloading for vehicular edge computing

Tang

Zhang

et al. 2021

J Cloud Comp

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“…The same scenario has to be followed when the processed results return. As a result, the MCC-based approach suffers from high transmission delays, data leakage, and compromised privacy due to the long-distance routing [ 8 ]. To reduce this network latency is very difficult if using the existing infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Hossain

Sultana

Hossain

et al. 2021

Sensors

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Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

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“…The fifth generation technology standard for broadband cellular networks, known as 5G, is being implemented to meet increasing service demand [8]. The related energy demand is under investigation [9,10].…”

Section: Introductionmentioning

confidence: 99%

Strategies for Improving the Sustainability of Data Centers via Practical and Effective Measures Such as Changing Energy Mix, Enhancing Energy Efficiency, Reducing Energy Use, and Circular Energy or Recycling Energy

Manganelli¹,

Soldati²,

Martirano³

et al. 2021

Preprint

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Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT infrastructures. This involves implementation and utilization of data centers, which increasingly contribute to energy and environmental impact. In this work, we address strategies to improve energy efficiency of data centers, to enhance sustainability towards energy resource and the environment. Among key strategies are innovative cooling technologies, use of natural resources, automation techniques, low-power electronics, and equipment with extended thermal limits. Research perspectives are identified and estimates of improvement opportunities are presented.

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Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

Cited by 347 publications

References 166 publications

Joint optimization of network selection and task offloading for vehicular edge computing

Joint optimization of network selection and task offloading for vehicular edge computing

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Strategies for Improving the Sustainability of Data Centers via Practical and Effective Measures Such as Changing Energy Mix, Enhancing Energy Efficiency, Reducing Energy Use, and Circular Energy or Recycling Energy

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