Interconnection network in HPC is becoming a bottleneck due to increasing traffic load. We model adaptive routing mechanisms and prove that even with advanced adaptive routing, static networks like Dragonfly cannot handle non-uniform traffic efficiently, let alone the frequently changing non-uniform traffic. Therefore, it requires architectural changes for network-wide improvements, e.g., reconfigurable networks.Existing reconfigurable networks hardly support agile reaction to traffic changes with little impact on network. Therefore, we propose MUSE1, a Dragonfly-based runtime incrementally reconfigurable network to enable a small number of link adjustments for agility and little impact on transmitting flows during every reconfiguration with optical circuit switch (OCS).Simulations with both synthetic traffic and real-world workloads prove that MUSE can prevent saturation under typical traffic patterns that cause congestion in static Dragonfly. MUSE is 30-55% better than static Dragonfly and Flexfly w.r.t commonly used performance metrics like flow completion time (FCT). We also build a MUSE prototype and demonstrate that MUSE enables 20-30% less application finish time (AFT).

Network experiments are essential to network-related scientific research (e.g., congestion control, QoS, network topology design, and traffic engineering). However, (re)configuring various topologies on a real testbed is expensive, time-consuming, and error-prone. In this paper, we propose Software Defined Topology Testbed (SDT), a method for constructing a user-defined network topology using a few commodity switches. SDT is low-cost, deployment-friendly, and reconfigurable, which can run multiple sets of experiments under different topologies by simply using different topology configuration files at the controller we designed. We implement a prototype of SDT and conduct numerous experiments. Evaluations show that SDT only introduces at most 2% extra overhead than full testbeds on multi-hop latency and is far more efficient than software simulators (reducing the evaluation time by up to 2899x). SDT is more cost-effective and scalable than existing Topology Projection (TP) solutions. Further experiments show that SDT can support various network research experiments at a low cost on topics including but not limited to topology design, congestion control, and traffic engineering.