In current Data Center Networks (DCNs), Equal- Cost MultiPath (ECMP) is used as the de-facto routing protocol. However, ECMP does not differentiate between short and long flows, the two main categories of flows depending on their duration (lifetime). This issue causes hot-spots in the network, affecting negatively the Flow Completion Time (FCT) and the throughput, the two key performance metrics in data center networks. Previous work on load balancing proposed solutions such as splitting long flows into short flows, using per-packet forwarding approaches, and isolating the paths of short and long flows. We propose DiffFlow, a new load balancing solution which detects long flows and forwards packets using Random Packet Spraying (RPS) with help of SDN, whereas the flows with small duration are forwarded with ECMP by default. The use of ECMP for short flows is reasonable, as it does not create the out-of-order problem; at the same time, RPS for long flows can efficiently help to load balancing the entire network, given that long flows represent most of the traffic in DCNs. The results show that our DiffFlow solution outperforms both the individual usage of either RPS or ECMP, while the overall throughput achieved is maintained at the level comparable to RPS.
Our solution was motivated by TinyFlow @cite_2 and RPS @cite_10 @cite_1 . The former (TinyFlow) splits long flows into short flows, and forwards them randomly by making use of ECMP. In order to detect long flows, OpenFlow switches perform sampling periodically. When two samples of the same flow are detected, the dynamic random re-routing algorithm changes the egress port of the switch. The latter method, i.e., RPS, uses random packet spraying technique to forward packets through multiple shortest paths. Unlike ECMP, RPS forwards each packet individually ("spraying") to the egress ports at the DCN switches. (Although this feature is not enabled by default, commodity switches can perform it.) The main drawback of this method is the out-of-order problem for TCP. As the authors prove, however, under the symmetry assumption in fat tree topologies and traffic patterns, this problem can be minimized, and even neglected. Also, custom queue management scheme can be applied to minimize the differences on path latencies.
@cite_1 @cite_10 @cite_2
1657185548 2117068155 1510598052 1986442642
Many existing data center network (DCN) flow scheduling schemes minimize flow completion times (FCT) based on prior knowledge of flows and custom switch functions, making them superior in performance but hard to use in practice. By contrast, we seek to minimize FCT with no prior knowledge and existing commodity switch hardware. To this end, we present PIAS, a DCN flow scheduling mechanism that aims to minimize FCT by mimicking Shortest Job First (SJF) on the premise that flow size is not known a priori. At its heart, PIAS leverages multiple priority queues available in existing commodity switches to implement a Multiple Level Feedback Queue (MLFQ), in which a PIAS flow is gradually demoted from higher-priority queues to lower-priority queues based on the number of bytes it has sent. As a result, short flows are likely to be finished in the first few high-priority queues and thus be prioritized over long flows in general, which enables PIAS to emulate SJF without knowing flow sizes beforehand. We have implemented a PIAS prototype and evaluated PIAS through both testbed experiments and ns- 2 simulations. We show that PIAS is readily deployable with commodity switches and backward compatible with legacy TCP IP stacks. Our evaluation results show that PIAS significantly outperforms existing information-agnostic schemes. For example, it reduces FCT by up to 50 and 40 over DCTCP [11] and L2DCT [27] respectively; and it only has a 4.9 performance gap to an ideal information-aware scheme, pFabric [13], for short flows under a production DCN workload. We consider a discrete-time stochastic model of an ECN RED gateway where competing TCP sources share the link capacity. As the number of competing flows becomes large, the asymptotic queue behavior (normalized by the number of flows) at the gateway can be described by a simple recursion and the throughput behavior of individual TCP flows becomes asymptotically independent. A Central Limit Theorem complement is also presented, yielding a more accurate characterization of the asymptotic queue size. These results suggest a scalable yet accurate model of this complex large-scale stochastic feedback system, and crisply reveal the sources of queue fluctuations. We study a processing system comprised of parallel queues, whose individual service rates are specified by a global service mode (configuration). The issue is how to switch the system between various possible service modes, so as to maximize its throughput and maintain stability under the most workload-intensive input traffic traces (arrival processes). Stability preserves the job inflow–outflow balance at each queue on the traffic traces. Two key families of service policies are shown to maximize throughput, under the mild condition that traffic traces have long-term average workload rates. In the first family of cone policies, the service mode is chosen based on the system backlog state belonging to a corresponding cone. Two distinct policy classes of that nature are investigated, MaxProduct and FastEmpty. In the second family of batch policies (BatchAdapt), jobs are collectively scheduled over adaptively chosen horizons, according to an asymptotically optimal, robust schedule. The issues of nonpreemptive job processing and non-negligible switching times between service modes are addressed. The analysis is extended to cover feed-forward networks of such processing systems nodes. The approach taken unifies and generalizes prior studies, by developing a general trace-based modeling framework (sample-path approach) for addressing the queueing stability problem. It treats the queueing structure as a deterministic dynamical system and analyzes directly its evolution trajectories. It does not require any probabilistic superstructure, which is typically used in previous approaches. Probability can be superposed later to address finer performance questions (e.g., delay). The throughput maximization problem is seen to be primarily of structural nature. The developed methodology appears to have broader applicability to other queueing systems. Several high-speed TCP variants are adopted by end users and therefore, heterogeneous congestion control has become the characteristic of newly emerging high-speed optical networks. In contrast to homogeneous TCP flows, fairness among heterogeneous TCP flows now depends on router parameters such as queue management scheme and buffer size. To the best of our knowledge, this is the first study of fairness performance among heterogeneous TCP variants over a 10 Gbps high-speed optical network environment. Our evaluation scenarios for heterogeneous TCP flows consist of TCP variants with substantial presence in current Internet; therefore, TCP-SACK, CUBIC TCP (CUBIC) and HighSpeed TCP (HSTCP) compete for bottleneck bandwidth. Experimental results for fairness are presented for different queue management schemes, such as Drop-tail, Random Early Detection (RED), CHOose and Keep for responsive flows CHOose and Kill for unresponsive flows (CHOKe), and Approximate Fair Dropping (AFD), with varying degree of buffer sizes. We observe heterogeneous TCP flows induce more unfairness than homogeneous ones. Active queue management (AQM) schemes, RED, CHOKe, and AFD, improve fairness for large buffer sizes as compared to Drop-tail, whereas AQM schemes lose the fairness advantage for small buffer sizes. This study provides preliminary results for fairness challenges to deploy all-optical routers with limited buffer size.