T2HT : TrafficDriven Machine Learning Based Hierarchical Topology Generation Model In highperformance computing (HPC) and distributed computing area, network performance greatly influenced application efficiency. However, due to the diversity of traffic patterns, the traditional network with fixed topology may achieve good performance under some applications, while performs poorly under other forms. Network reconfiguration technologies which can change the topology dynamically have been developed to obtain a balanced performance for different traffic patterns. Nonetheless, selecting an appropriate network topology from the wide variety of options remains difficult due to the complexity of analyzing traffic alongside topology performance characteristics. Traditional research focused on congestion estimation and specific parameter adjustment without reconfiguring the global topology. In this paper, we propose a generic Traffic to Hierarchical Topology (T2HT) method to analyze traffic patterns and choose an appropriate network configuration for the given traffic T2HT makes use of actual traffic data with a hierarchical model to predict network performance with a given topology and uses a machine learning (ML) algorithm to score the better options in order to determine the best topology. We performed 8000 simulations of datasettopology combinations to verify the feasibility of the model. Our results show that T2HT achieved marked improvements with its recommendations, making it feasible for use in hierarchical network design. Under the DOE testbed, the throughput of the topology generated by T2HT can reach above 90% of theoretical limit(full connection), and the latency is improved by about 24.6% compared to typical topology 3D Torus with the same physical restrictions.

G2: A Network Optimization Framework for HighPrecision Analysis of Bottleneck and Flow Performance Congestion control algorithms for data networks have been the subject of intense research for the last three decades. While most of the work has focused around the characterization of a flowu0027s bottleneck link, understanding the interactions amongst links and the ripple effects that perturbations in a link can cause on the rest of the network has remained much less understood. The Theory of Bottleneck Ordering is a recently developed mathematical framework that reveals the bottleneck structure of a network and provides a model to understand such effects. In this paper we present G2, the first operational network optimization framework that utilizes this new theoretical framework to characterize with highprecision the performance of bottlenecks and flows. G2 generates an interactive graph structure that describes how perturbations in links and flows propagate, providing operators new optimization insights and traffic engineering recommendations to help improve network performance. We provide a description of the G2 implementation and a set of experiments using production TCPIP code to demonstrate its operational efficacy.