On the impact of clustering for IoT analytics and message broker placement across cloud and edge

Abstract

With edge computing emerging as a promising solution to cope with the challenges of Internet of Things (IoT) systems, there is an increasing need to automate the deployment of large-scale applications along with the publish/subscribe brokers they communicate over. Such a placement must adjust to the resource requirements of both applications and brokers in the heterogeneous environment of edge, fog, and cloud. In contrast to prior work focusing only on the placement of applications, this paper addresses the problem of jointly placing IoT applications and the pub/sub brokers on a set of network nodes, considering an application provider who aims at minimizing total end-to-end delays of all its subscribers. More specifically, we devise two heuristics for joint deployment of brokers and applications and analyze their performance in comparison to the current cloud-based IoT solutions wherein both the IoT applications and the brokers are located solely in the cloud. As an application provider should consider not only the location of the application users but also how they are distributed across different network components, we use von Mises distributions to model the degree of clustering of the users of an IoT application. Our simulations show that superior performance of our heuristics in comparison to cloud-based IoT operation is most pronounced under a high degree of clustering. When users of an IoT application are in close network proximity of the IoT sensors, cloud-based IoT unnecessarily introduces latency to move the data from the edge to the cloud and vice versa while processing could be performed at the edge or the fog layers.