SRPT-based Congestion Control for Flows with Unknown Sizes

Modern datacenter transports are required to support latency constraints, usually represented by various forms of flow completion time (FCT). Most implemented congestion control mechanisms that minimize FCT are based on SRPT priorities (e.g., pFabric and Homa). However, SRPT-based scheduling requires prior knowledge of flow sizes, making this discipline problematic in general. Non-SRPT-based alternatives such as LAS and PIAS are able to cope with this level of uncertainty but suffer from their own limitations: LAS can lead to significant starvation of concurrent elephant flows, while PIAS requires a centralized entity for correct settings. In this work, we generalize SRPT-based scheduling to allow flows with known and unknown sizes to sojourn at the same time. We not only show analytic properties of this generalization but rigorously prove important properties of non-SRPT alternatives with competitive analysis. Based on the proposed SRPT generalization, we introduce a new ASCC congestion control. Our main goal is not to propose yet another congestion control but to identify preferable and pathological traffic patterns with unknown flow sizes for various scheduling disciplines. Our observations are validated by an extensive evaluation study.