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http://dx.doi.org/10.9717/kmms.2022.25.8.1136

Flow Prediction-Based Dynamic Clustering Method for Traffic Distribution in Edge Computing  

Lee, Chang Woo (The School of Software, Kookmin University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.8, 2022 , pp. 1136-1140 More about this Journal
Abstract
This paper is a method for efficient traffic prediction in mobile edge computing, where many studies have recently been conducted. For distributed processing in mobile edge computing, tasks offloading from each mobile edge must be processed within the limited computing power of the edge. As a result, in the mobile nodes, it is necessary to efficiently select the surrounding edge server in consideration of performance dynamically. This paper aims to suggest the efficient clustering method by selecting edges in a cloud environment and predicting mobile traffic. Then, our dynamic clustering method is to reduce offloading overload to the edge server when offloading required by mobile terminals affects the performance of the edge server compared with the existing offloading schemes.
Keywords
Mobile Edge Computing; Traffic Flow; Clustering; Machine Learning; AI;
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