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.

Modern network elements are increasingly required to deal with heterogeneous traffic. Recent works consider processing policies for buffers that hold packets with different processing requirements (number of processing cycles needed before a packet can be transmitted out) but uniform value, aiming to maximize the throughput, i.e., the number of transmitted packets. Other developments deal with packets of varying value but uniform processing requirement (each packet requires one processing cycle); the objective here is to maximize the total transmitted value. In this paper, we consider a more general problem, combining packets with both nonuniform processing and nonuniform values in the same queue. We study the properties of various processing orders in this setting. We show that in the general case, natural processing policies have poor performance guarantees, with linear lower bounds on their competitive ratio. Moreover, we show several adversarial lower bounds for every priority queue and even for every online policy. On the positive side, in the special case when only two different values are allowed, 1 and V , we present a policy that achieves competitive ratio (1 + (W + 2/V )), where W is the maximal number of required processing cycles. We also consider copying costs during admission.

Efficient representation of data aggregations is a fundamental problem in modern big data applications. We present a formalization of compute-aggregate planning parameterized by the aggregation function.

Modern datacenters are increasingly required to deal with latency-sensitive applications. A major question here is how to represent latency in desired objectives. Incorporation of multiple traffic characteristics (e.g., packet values and required processing requirements) significantly increases the complexity of buffer management policies. In this work, we consider weighted throughput optimization (total transmitted value) in the setting where every incoming packet is branded with intrinsic value, required processing, and slack (an offset from the arrival time when a packet should be transmitted), and the buffer is unbounded but effectively bounded by slacks. The main result is a 3-competitive algorithm as the slack-to-work ratio increases. Our results supported by a comprehensive evaluation study on CAIDA network traces.