Optimal deployment of cloudlets based on cost and latency in Internet of Things networks
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Optimal deployment of cloudlets based on cost and latency in Internet of Things networks Zhongmin Wang1,2 • Feng Gao2
•
Xiaomin Jin1,2
Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the development of the Internet of Things (IoT), a large amount of data is generated on the network edge. Given the limited computing power of mobile devices (MDs) and access to computing resources from remote clouds, which leads to high latency to MDs, edge computing provides a way to reduce service latency by building a miniature cloud (Cloudlet). MDs transfer tasks they generate to nearby cloudlets for lower latency. Although a lot of research has been done in the field of edge computing, little attention has been paid to how to deploy cloudlets in the network. In this paper, we study the cloudlet deployment on a large number of wireless access points (APs) in an IoT network to optimize both deployment cost and network latency. When the cloudlets has been deployed in the network, we propose a fault-tolerant cloudlet deployment scheme. When the original cloudlets in the network fail, the software-defined network technology is used to start the fault-tolerant cloudlets in time to ensure the stability of the network latency. To address the above problems, we propose a binary-based differential evolution cuckoo search (BDECS) algorithm, which selects the permanent cloudlet deployment location among a large number of APs on the network. Extensive simulations reveal that the proposed algorithm has better performance in minimizing cost and latency compared with other deploymegt algorithms. Moreover, the convergence speed of the BDECS algorithm is also superior to other algorithms. Keywords Cloudlets deployment Internet of Things (IoT)networks Edge computing Cost Latency
1 Introduction With the arrival of the Internet of Everything (IoE) era, linearly growing centralized cloud computing capabilities cannot match the exponential growth of data generated by network edge-side terminals [1]. Massive data access to the cloud computing center on the network edge will consume large network bandwidth and generate high network latency. Faced with this dilemma, edge computing, as a new computing model, bridges the Internet of Things (IoT) devices and data centers. It enables data to be processed in
& Feng Gao [email protected] 1
School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, Shaanxi, China
2
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an 710121, Shaanxi, China
a timely and effective manner near the source of generation. Cloudlet [2] is an emerging framework in the edge computing filed. Deployed on the network edge and connected to the Internet, it is a trusted host with relatively abundant computing resources, which is accessed by mobile devices (MDs) and provides services. Using cloudlet as the supplement of w
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