An Improved Fast Search and Find of Density Peaks-Based Fog Node Location of Fog Computing System
Xiaoqun Yuan, He Yan, Qing Fang, Xiaowen Tong, Changlai Du, Yi Ding · 2017
As the most notably emerging wave of Internet deployments, Internet of Things (IoTs) requires mobility support, location awareness and low latency. Fog Computing, also termed edge computing, is a promising solution for IoTs by extending the Cloud Computing paradigm to the edge of Internet. But how to locate fog nodes' sites and determine the scale of each fog node is a main challenge of Fog Computing systems, especially for time sensitive Fog Computing systems. In this paper, we try to deal with this problem by proposing an improved Fast Search and Find of Density Peaks-based fog node location strategy to locate the fog nodes' sites and determine the resources for each located fog node. To this end, we firstly formulate the fog node location of Fog Computing systems as a clustering problem with multi-constraints. Then we propose an improved Fast Search and Find of Density Peaks-based fog node location algorithm, which introduces the time sensitive feature of IoT applications and improves the Fast Search and Find of Density Peaks clustering algorithm to make this clustering algorithm more robustness and adaptability. The experiment results show that our fog node location strategy not also can avoid the NP-hard problem of the traditional server placement strategies, but also has low time complexity.