Performance Analysis of a Graph-Theoretic Load Balancing Method for Data Centers

Abstract

Modern data centers can process a massive amount of data in a short time with minimal errors. Data center networks (DCNs) use equal-cost, multi-path topologies to deliver split flows across alternative paths between the core layer and hosted servers, which could lead to significant overload if path scheduling is inefficient. Thus, distributing incoming requests among these paths is crucial for providing higher throughput and protection against link or switch failures. Several approaches have been proposed for path selection, mainly relying on round-robin and least-congested methods. In this paper, we propose a loadbalancing method based on betweenness centrality to improve the overall performance of a data center in terms of throughput, delay, and energy consumption. For evaluation, we compare our method with baseline methods of different DCN topologies: fattree, DCell, and BCube. On average, the evaluation results show that our method outperforms the others. It increases throughput by 202% and 33% while reducing delay by 20% and 22%, and energy consumption by 40% and 41% compared to the roundrobin and least-congested methods, respectively.

Evaluation of industrial network robustness against targeted attacks

“…8,9 In recent years, its use for quantifying resilience has evolved to become an essential component in multiple applications within the field of computer network security. [10][11][12][13][14] One of the main resilience metrics, referred to as robustness, is the ability of a network to keep providing the same level of service in the presence of disruptions. 6,7 In general, resilience is determined by centrality measures that quantify the significance of nodes; robustness is evaluated after the node with the highest centrality measure has been attacked.…”

“…Graph theory has long been used for network analysis, including designing, modeling, and optimizing networks 8,9 . In recent years, its use for quantifying resilience has evolved to become an essential component in multiple applications within the field of computer network security 10‐14 . One of the main resilience metrics, referred to as robustness, is the ability of a network to keep providing the same level of service in the presence of disruptions 6,7 .…”

A RED-BET Method to Improve the Information Diffusion on Social Networks

“…Graph reduction, based on the application of graph theory [31], [36], is the basic and effective technique to minimize the time to calculate parameters in graphs and the time to analyze information diffusion forecast. Reducing unimportant vertices and edges on the graph will make the calculation faster.…”