Adia: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks

Abstract

Link utilization has received extensive attention since data centers become the most pervasive platform for data-parallel applications. A specific job of such applications involves communication among multiple machines. The recently proposed coflow abstraction depicts such communication through a group of parallel flows, and captures application performance through corresponding communication requirements. Existing techniques to improve link utilization, however, either restrict themselves to achieving work conservation, or merely focus on flow-level metrics and ignore coflow-level performance. In this paper, we address the coflow-aware scheduling problem with the objective of maximizing link utilization. Through theoretic analyses, we formulate the coflow-aware scheduling problem as a NP-hard open shop scheduling problem with heterogeneous concurrency. We design Adia, a hierarchical scheduling framework to conduct both inter- and intra- link scheduling. The design of Adia leverages priority-based scheduling while guarantees work-conserving and starvation-free bandwidth allocation at the same time. We also prove Adia's algorithm is two-approximate in terms of link utilization. Extensive simulation results on ns3 further show that Adia outperforms both per-flow mechanisms coflow schemes in terms of link utilization, and achieves similar coflow performance in comparison with the state-of-art coflow scheduling schemes.