Enabling Fog Computing using Self-Organizing Compute Nodes

Abstract

The emergence of fog computing has led to the design of multi-layer fog computing models which are organized hierarchically. These models commonly dictate the hierarchical structure to all the participating compute nodes. However, organizing the compute nodes by adding customized connections that do not abide by the hierarchical approach, may result in improved performance due to the network's properties i.e., latency or bandwidth between the nodes. For this reason, in this paper we propose an alternative to the hierarchical approach, which is the self-organizing compute nodes. These nodes organize themselves into a flat model which leverages on the network's properties to provide improved performance. The results of the evaluation show that this approach reduces bandwidth utilization (∼30%) by using optimized messaging instead of direct messaging. Furthermore, we show that following a flat model, enables the design of mechanisms for fault tolerance which has been mostly neglected in existing hierarchical models.

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

“…Before hosting trust models, suitable architecture for Fog computing is needed to provide scalability, fast data access, simple and efficient intra-communication, load balancing, decentralization, and availability. As we write, horizontal Fog architectures have been adopted by many researchers and professional organizations [35][36][37]. In 2018, IEEE adopted OpenFog [38] as the reference architecture for Fog Computing.…”

“…Horizontal Fog architectures have been proposed by many researchers and professional organizations [35][36][37][52][53][54][55]. In 2018, IEEE adopted OpenFog [38] as the reference architecture for Fog Computing.…”

“…The data are processed in each Fog node and then sent to neighbor nodes and cloud servers. A research in the field of horizontal Fog architectures was proposed by [ 37 ]. The proposed architecture organizes Fog nodes following a model leveraging the network’s resources to provide improved performance.…”

Enabling Fog-based Industrial Robotics Systems

“…Another aspect of the proposed use case is scalability [44]. In an industrial setting (as discussed in Section III), the number of participating compute nodes can become very large depending on the size of the factory, because compute nodes may include cloud and fog nodes as well as the interface devices.…”

“…Along with the potential benefits of interconnected factories, and the advantages that fog computing will bring to industrial environments, there are also related concerns. For example, the communication overhead from the interactions among the distributed cloud and fog nodes, needs to remain limited so that it does not interfere with the operation of the applications [44], and it does not compromise the functionality of nodes which execute tasks with strict deadlines (see Section IV-A). Most applicable fog computing approaches aim at enabling computing close to the edge of the network [45].…”