Workload adaptive flow scheduling

ABSTRACTExisting flow scheduling schemes for data center networks optimize for a specific workload and performance metric. In this paper, we present 2D, a new scheduling policy that offers robustness across performance metrics and changing workloads - a ground existing scheduling policies are unable to cover. 2D combines basic scheduling building blocks of multiplexing and serialization in a principled way, ensuring tail optimal performance across workloads while also improving the average (and lower percentiles) completion times. To implement 2D for flow-level scheduling in a distributed setting, we break-up the scheduling decision into two parts: coarse time-scale decisions based on workload and load changes are made by a centralized controller while per-flow serialization decisions are made in a distributed fashion, involving the end-points and sequencer(s). Our testbed experiments show that, for realistic cloud workloads, 2D provides consistent gains at the tail and average flow completion times compared to basic scheduling techniques (e.g., FIFO and processor sharing) as well as heuristic-based schedulers (e.g., Aalo and Baraat).