Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks need...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 11; no. 1; p. 58
Main Authors Pham, Xuan-Qui, Nguyen, Tien-Dung, Nguyen, VanDung, Huh, Eui-Nam
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2019
Subjects
Cloud computing
Completion time
Computation offloading
Edge computing
Internet of Things
Medical equipment
Mixed integer
mobile cloud computing
Mobile computing
multi-access edge computing
Nodes
Nonlinear programming
Optimization
Resource allocation
Sensors
task offloading
Traffic congestion
Vehicles
vehicular cloud
Wide area networks
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Summary:The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym11010058