Important datacenter applications generate extremely bursty traffic patterns and demand low latency tails as well as high throughput. Datacenter networks employ shallow-buffered, shared-memory switches to cut cost and to cope up with ever-increasing link speeds. End-to-end congestion control cannot react in time to handle bursty, short flows that dominate datacenter traffic and they incur buffer overflows, which cause long latency tails and degrade throughput. Therefore, there is a need for agile, switch-local mechanisms that quickly sense congestion and provision enough buffer space dynamically to avoid costly buffer overflows. We propose Smartbuf, an online learning algorithm that accurately predicts buffer requirement of each switch port before the onset of congestion. Our key novelty lies in fingerprinting bursts based on the gradient of queue length and using this information to provision just enough buffer space. Our preliminary evaluations show that our algorithm can predict buffer demands accurately within an average error margin of 6% and achieve an improvement in the 99thpercentile latency by a factor of 8x at high loads, while providing good fairness among ports.

Several important datacenter applications cause incast congestion, which severely degrades flow completion times of short flows and throughput of long flows. Further, because most flows are short and the incast duration is shorter than typical round-trip times, reactive mechanisms that rely on congestion control are not effective. While modern datacenter topologies provide high bisection bandwidth to support all-to-all traffic, incast is fundamentally a many-to-one traffic pattern, and therefore, requires deep buffers or high bandwidth at the network edge.

Datacenters host a mix of applications: foreground applications perform distributed lookups in order to service user queries and background applications perform batch processing tasks such as data reorganization, backup, and replication. While background flows produce the most load, foreground applications produce the most number of flows. Because packets from both types of applications compete at switches for network bandwidth, the performance of applications is sensitive to scheduling mechanisms. Existing schedulers use flow size to distinguish critical flows from non-critical flows. However, recent studies on datacenter workloads reveal that most flows are small (e.g., most flows consist of only a handful number of packets). In light of recent findings, we make the key observation that because most flows are small, flow size is not sufficient to distinguish critical flows from non-critical flows and therefore existing flow schedulers do not achieve the desired prioritization. In this paper, we introduce ResQueue, which uses a combination of flow size and packet history to calculate the priority of each flow. Our evaluation shows that ResQueue improves tail flow completion times of short flows by up to 60% over the state-of-the-art flow scheduling mechanisms.

Datacenter applications frequently cause incast congestion, which degrades short flows' flow completion times and long flows' throughput. Existing congestion control schemes (e.g., DCTCP) do not explicitly detect and isolate incast. Instead, they rely on existing Explicit Congestion Notification (ECN) to react to general congestion. They, therefore, lose performance due to slow, cautious, and inaccurate reaction to incast. We propose a novel algorithm that detects incasts and notifies senders using a new Explicit Incast Notification EIN). We show that our incast detection is fast and accurate. Next, we present our congestion control scheme, called Pulser , which isolates incasts using EIN. Unlike DCTCP, which gradually adjusts sending rate, Pulser drastically backs off during incast and rapidly restores sending rate once incast ends (i.e., like a pulse). Our real experiments and ns-3 simulations show that Pulser outperforms prior schemes, DCTCP and ICTCP, in both flow completion times and throughput.

Datacenter applications demand both low latency and high throughput; while interactive applications (e.g., WebSearch) demand low tail latency for their short messages due to their partition-aggregate software architecture, many data-intensive applications (e.g., Map-Reduce) require high throughput for long flows as they move vast amounts of data across the network. Recent proposals improve latency of short flows and throughput of long flows by addressing the shortcomings of existing packet scheduling and congestion control algorithms, respectively. We make the key observation that long tails in theFlow Completion Times (FCT) of short flows result from packetsthat suffer congestion at more than one switch along their paths in the network. Our proposal,Slytherin, specifically targets packets that suffered from congestion at multiple points and prioritizes them in the network. Slytherin leverages ECN mechanism which iswidely used in existing datacenters to identify such tail packets and dynamically prioritizes them using existing priority queues. As compared to existing state-of-the-art packet scheduling proposals, Slytherin achieves 18.6% lower 99th percentile flow completion times for short flows without any loss of throughput. Further, Slytherin drastically reduces 99th percentile queue length in switches by a factor of about 2x on average.

In present paper, an active fault tolerant control (FTC) system is proposed for three phase induction motor (IM) drives subjected to the mechanical faults caused by both stator and rotor failures. FTC design consists of two main parts. The first part is design of a nominal controller in the fault-free case to achieve control objectives. In this work, a feedback linearizing controller has been employed. The second one is the design of a sliding mode observer (SMO) in order to estimate additive faults which model mechanical faults and production of extra control inputs for compensating their undesirable effects on IM performance. SMO is also used for online estimation of axial fluxes in the both conditions. The simulations are performed using Matlab/Simulink software to illustrate the effectiveness of the proposed FTC scheme.