Abstract
Cloud computing technology is widely used at present. However, cloud computing servers are far from terminal users, which may lead to high service request delays and low user satisfaction. As a new computing architecture, fog computing is an extension of cloud computing that can effectively solve the aforementioned problems. Resource scheduling is one of the key technologies in fog computing. We propose a resource scheduling method for fog computing in this paper. First, we standardize and normalize the resource attributes. Second, we combine the methods of fuzzy clustering with particle swarm optimization to divide the resources, and the scale of the resource search is reduced. Finally, we propose a new resource scheduling algorithm based on optimized fuzzy clustering. The experimental results show that our method can improve user satisfaction and the efficiency of resource scheduling.
Highlights
With the rapid development of technology related to the Internet of Things (IoT) [1], an increasing amount of data is being transmitted through the Internet
The remainder of this paper is organized as follows: the related work is discussed in the Section 2, the fog computing architecture is described in the Section 3, the resource scheduling problem in fog computing is described in the Section 4, the resource scheduling algorithms are proposed in the Section 5, the experimental results are analyzed in the Section 6, and Section 7 summarizes the full text
The inertia weight in the particle swarm optimization algorithm is linearly decremented according to Equation (14)
Summary
With the rapid development of technology related to the Internet of Things (IoT) [1], an increasing amount of data is being transmitted through the Internet. As a distributed computing model, can store and process the massive data generated by the IoT and provide terminal users with reliable services [2,3]. Smart devices consume a large amount of network bandwidth and aggravate the burden of cloud data centers [4,5], such that some delay-sensitive services in the IoT cannot be responded to and processed quickly. Fog computing is located between the IoT devices and cloud servers, and provides computing and storage services at the edge of the Internet. When a service request is raised by the terminal device in the network, first, data filtering, preprocessing, and analysis are performed in fog computing. We chose the improved fuzzy clustering algorithm to solve the resource scheduling problem in fog computing. The remainder of this paper is organized as follows: the related work is discussed in the Section 2, the fog computing architecture is described in the Section 3, the resource scheduling problem in fog computing is described in the Section 4, the resource scheduling algorithms are proposed in the Section 5, the experimental results are analyzed in the Section 6, and Section 7 summarizes the full text
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