Distributed algorithms based on proximity for self-organizing fog computing systems

Abstract

Various performance benefits such as low latency and high bandwidth have turned fog computing into a well-accepted extension of the cloud computing paradigm. Many fog computing systems have been proposed so far, consisting of distributed compute nodes which are often organized hierarchically in layers. To achieve low latency, these systems commonly rely on the assumption that the nodes of adjacent layers reside close to each other. However, this assumption may not hold in fog computing systems that span over large geographical areas, due to the wide distribution of the nodes. To avoid relying on this assumption, in this paper we design distributed algorithms whereby the compute nodes measure the network proximity to each other, and self-organize into a hierarchical or a flat structure accordingly. Moreover, we implement these algorithms on geographically distributed compute nodes, and we experiment with image processing and smart city use cases. Our results show that compared to alternative methods, the proposed algorithms decrease the communication latency of latency-sensitive processes by 27%–43%, and increase the available network bandwidth by 36%–86%. Furthermore, we analyze the scalability of our algorithms, and we show that a flat structure (i.e., without layers) scales better than the commonly used layered hierarchy due to generating less overhead when the size of the system grows.