HOMMO: A Hierarchical Flow Management Framework for Multi-Objective Data Center Networks

In data center networks, flows with different objectives coexist and compete for limited resources. From the application-level perspective, it is hard to satisfy the demands of different flows without effective resource planning. To address the bandwidth allocation problem under multi-objective scenarios, we study a multi-objective network utility maximization (NUM) problem and propose our practical bandwidth allocation framework HOMMO, which consists of an upper layer algorithm (ULA) and a lower layer algorithm (LLA). This hierarchical design helps to strike a balance between accuracy and efficiency. Implemented in network switches, ULA is an online learning-based scheme that allocates bandwidth for aggregated flows with different performances objectives. Taking the outputs from ULA as capacity constraints, LLA acts as a fast scheduling method at packet-level. We extend NUMFabric by implementing the design of isolate queues and corresponding dequeue strategy in switches and make it a feasible solution of the LLA. Therefore, HOMMO achieves satisfactory isolation across flows with different objectives, which is equivalent to provide a network slicing solution. To evaluate the proposed framework, we implement it in ns-3 and verify the performance under various scenarios. The simulation results show that HOMMO not only quickly converges to a near-optimal solution of the multi-objective NUM problem but also guarantees a Pareto-optimal solution. Moreover, it outperforms the well-known transport protocol (i.e., DCTCP) with 2x increase on average bandwidth utilization and 1.96x improvement in global network utility at the aggregated flow level.