Data centers house some of the largest, fastest networks in the world. In contrast to and as a result of their speed, these networks operate on very small timescales---a 100 Gbps port processes a single packet in at most 500 ns with end-to-end network latencies of under a millisecond. In this study, we explore the fine-grained behaviors of a large production data center using extremely high-resolution measurements (10s to 100s of microsecond) of rack-level traffic. Our results show that characterizing network events like congestion and synchronized behavior in data centers does indeed require the use of such measurements. In fact, we observe that more than 70% of bursts on the racks we measured are sustained for at most tens of microseconds: a range that is orders of magnitude higher-resolution than most deployed measurement frameworks. Congestion events observed by less granular measurements are likely collections of smaller μbursts. Thus, we find that traffic at the edge is significantly less balanced than other metrics might suggest. Beyond the implications for measurement granularity, we hope these results will inform future data center load balancing and congestion control protocols.

Many data center traffic patterns exhibit abundant concurrent connections and high churn. In the face of these characteristics, server-centric congestion control is a poor fit—each connection, no matter how small, must start from scratch when testing when and how much to send along a given path. This is despite the fact that there are a large number of flows that may have already probed the same exact path, not just at a server level, but also at a rack level. Thus, we argue for rack-level congestion control in which all connections are tunneled through rack-to-rack JumboFlows. This design allows an entire rack’s connections to cooperate with one another for better fairness and performance, particularly for short flows. In this paper, we examine situations in which JumboFlows might be useful and present a preliminary design of a system (RackCC) that implements JumboFlows.

Updating a large network deployment is a dangerous process. Regardless of whether the operation is a switch BGP configuration change or a network-wide SDN controller upgrade, misconfigurations and bugs can potentially cause network outages and downtime for critical cloud services. Many cloud applications have adopted a useful strategy that networks have tried to emulate: phased rollouts, in which a small fraction of users are redirected to the updated version while most users continue unassailed. Unfortunately, this analogy is fundamentally flawed. This paper explores the limits of phased rollouts in networks and shows when and why they can fail. We also go on to propose two preliminary designs for approximating the benefits of phased rollouts. Although preliminary, we argue that our designs can achieve a useful level of isolation between a ‘known correct’ and ‘new’ control plane.

There has been recent interest in designing systems that use the tongue as an input interface. Prior work however either require surgical procedures or in-mouth sensor placements. In this paper, we introduce TongueSee, a non-intrusive tongue machine interface that can recognize a rich set of tongue gestures using electromyography (EMG) signals from the surface of the skin. We demonstrate the feasibility and robustness of TongueSee with experimental studies to classify six tongue gestures across eight participants. TongueSee achieves a classification accuracy of 94.17% and a false positive probability of 0.000358 per second using three-protrusion preamble design.

A longstanding problem with the Internet is that it is vulnerable to outages, black holes, hijacking and denial of service. Although architectural solutions have been proposed to address many of these issues, they have had difficulty being adopted due to the need for widespread adoption before most users would see any benefit. This is especially relevant as the Internet is increasingly used for applications where correct and continuous operation is essential.