Edge Data Center Organization and Optimization by Using Cage Graphs

Abstract

Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers.

Study on Data Center Network Topologies for Monitoring Data using Edge/Fog Computing

“…Some typical examples commonly used in switch-centric data center topologies are Fat Tree and Leaf & Spine, whereas some instances typically employed in servercentric data center architectures are BCube, DCell and FiConn [75]. Therefore, those five topologies are going to be studied in order to calculate de average number of links between any given pair of nodes within each topology [76], along with some typical centralization and dispersion statistics related to them, in a way to look into the performance offered by each of those instances [77] .…”