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Symmetry 2019, 11(1), 58; https://doi.org/10.3390/sym11010058

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

Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea
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Received: 6 December 2018 / Revised: 25 December 2018 / Accepted: 2 January 2019 / Published: 7 January 2019
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Abstract

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. View Full-Text
Keywords: task offloading; resource allocation; mobile cloud computing; multi-access edge computing; vehicular cloud; Internet of Things task offloading; resource allocation; mobile cloud computing; multi-access edge computing; vehicular cloud; Internet of Things
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Pham, X.-Q.; Nguyen, T.-D.; Nguyen, V.; Huh, E.-N. Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing. Symmetry 2019, 11, 58.

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