Next Article in Journal
Gas Sensors Based on Mechanically Exfoliated MoS2 Nanosheets for Room-Temperature NO2 Detection
Previous Article in Journal
An Efficient Hybrid RSS-AoA Localization for 3D Wireless Sensor Networks
Previous Article in Special Issue
On the Combination of Multi-Cloud and Network Coding for Cost-Efficient Storage in Industrial Applications
Article Menu
Issue 9 (May-1) cover image

Export Article

Version is current.

Open AccessArticle

Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing

School of Information Science and Engineering, Qufu Normal University, Rizhao 276800, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2122; https://doi.org/10.3390/s19092122
Received: 18 March 2019 / Revised: 30 April 2019 / Accepted: 1 May 2019 / Published: 8 May 2019
(This article belongs to the Special Issue Recent Advances in Fog/Edge Computing in Internet of Things)
  |  
PDF [2429 KB, uploaded 8 May 2019]
  |  

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. View Full-Text
Keywords: fog computing; fuzzy c-means algorithm; particle swarm optimization; resource clustering; resource scheduling fog computing; fuzzy c-means algorithm; particle swarm optimization; resource clustering; resource scheduling
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, G.; Liu, Y.; Wu, J.; Lin, D.; Zhao, S. Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing. Sensors 2019, 19, 2122.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top