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.
Highlights
During the last decade, we witnessed a striking rise in population of mobile Internet of Things (IoT) devices, such as smart phones, tablets, wearable devices, and sensors
We investigate an innovative framework of task offloading in a scalable vehicle-assisted multi-access edge computing (MEC)
We evaluate the offloading performance in terms of total computation overhead, which is the weighted sum of task completion time and monetary cost for using computing resources
Summary
We witnessed a striking rise in population of mobile Internet of Things (IoT) devices, such as smart phones, tablets, wearable devices, and sensors. By integrating various usable resources of volunteer vehicles, we can further relieve the resource limitation of MEC, enhance the scalability of computing services for IoT devices and reduce the cost of using cloud resources. In this paper, we propose to combine MEC with fixed remote cloud and vehicular cloud to expand the currently available resources of MEC for task requests from IoT devices. IoT devices, the MEC provider makes a strategy to allocate these computation tasks to a computing platform originated from the infrastructure of MEC, remote cloud, and vehicular cloud. In the vehicular cloud, we use buses in the VC as candidate mobile volunteer nodes (MVNs) that share their idle computing resources for tasks offloaded from the MEC provider.
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