An offloading method using decentralized P2P-enabled mobile edge servers in edge computing

Tang, Wenda; Zhao, Xuan; Rafique, Wajid; Qi, Lianyong; Dou, Wanchun; Ni, Qiang

doi:10.1016/j.sysarc.2019.02.001

Cited by 45 publications

(38 citation statements)

References 38 publications

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“…9. E. Ahmed, " Recent advances in fog and mobile edge computing," https://www.researchgate.net/ publication/324397810_Recent_advances_in_fog_a nd_mobile_edge_computing Figure 1: A typical mobile edge computing architecture [6].…”

Section: Resultsmentioning

confidence: 99%

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Mobile Edge Computing

Sadiku¹,

Kotteti²,

Musa³

2019

GJCST

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Mobile applications are becoming increasingly computationalintensive, while many mobile devices still have limited battery power and cannot support computational intensive tasks. Mobile edge computing (MEC) computing is an extension of edge computing, and it refers to computing at the edge of a network. In mobile edge computing, computing and storage nodes are placed at the Internet's edge near mobile devices. It places the edge clouds at the candidate locations. This paper presents a brief introduction to MEC.Abstract-Mobile applications are becoming increasingly computational-intensive, while many mobile devices still have limited battery power and cannot support computational intensive tasks. Mobile edge computing (MEC) computing is an extension of edge computing, and it refers to computing at the edge of a network. In mobile edge computing, computing and storage nodes are placed at the Internet's edge near mobile devices. It places the edge clouds at the candidate locations. This paper presents a brief introduction to MEC.

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Section: Resultsmentioning

confidence: 99%

“…MEC can offer a service environment with ultralow latency, high-bandwidth, and direct access to real-time network information [5]. A typical mobile edge computing architecture is shown in Figure 1 [6].…”

Section: Concept Of Mobile Edge Computingmentioning

confidence: 99%

Mobile Edge Computing

Sadiku¹,

Kotteti²,

Musa³

2019

GJCST

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“…In recent years, several studies have addressed the mobile task offloading in the MEC scenario. The task offloading can be classified into several aspects according to optimization objectives, includes: delay or latency constrained offloading [15,16], energy-efficient offloading [17,18], energy-latency tradeoff for offloading [19,20], cost-efficient offloading [21], etc. Generally speaking, the offloading is a multi-objective optimization problem, which is usually solved by optimized or heuristic algorithms.…”

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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“…This model uses the basic hierarchical topology of the Internet and combines random early detection algorithms to avoid congestion. Tang et al [26] proposed an offloading method that uses distributed peer‐to‐peer networks to support mobile edge servers in edge computing and manage geographically distributed mobile edge servers. A deadline‐aware, cost‐effective offloading scheme was proposed to improve the computational offload efficiency of the vehicle and meet deadlines for other tasks.…”

Section: Related Workmentioning

confidence: 99%

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

Zeng

Fang

et al. 2020

IET Communications

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Mobile edge computing (MEC) is concerned with moving complex tasks from data sources to nearby computing resources, which can reduce computing latency and remote cloud workload. Although there has been significant research in the field of MEC, research on edge server placement in wireless metropolitan area networks (WMANs) is overlooked, and the load balancing problem of edge servers is seldom discussed. From a practical perspective, how to place edge servers efficiently in WMANs while considering load balancing between edge servers is studied. A greedy algorithm is proposed that can balance the workload of edge servers more effectively. However, the performance of the greedy algorithm as the number of servers placed increases is not ideal. Therefore, the authors combine the greedy algorithm with a genetic algorithm (GA) to minimise the number of edge servers while ensuring load balancing between edge servers and quality of service (QoS) requirements for mobile users. Finally, they conduct simulation experiments and compare the proposed algorithms with other algorithms. The improved GA proposed is superior to the greedy algorithm in terms of load balancing and the number of servers. The experimental results demonstrate that the algorithm has excellent performance.

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An offloading method using decentralized P2P-enabled mobile edge servers in edge computing

Cited by 45 publications

References 38 publications

Mobile Edge Computing

Mobile Edge Computing

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

Load balancing edge server placement method with QoS requirements in wireless metropolitan area networks

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