No abstract

For provisioning large-scale online applications such as web search, social networks and advertisement systems, data centers face extreme challenges in providing low latency for short flows (that result from end-user actions) and high throughput for background flows (that are needed to maintain data consistency and structure across massively distributed systems). We propose L 2 DCT, a practical data center transport protocol that targets a reduction in flow completion times for short flows by approximating the Least Attained Service (LAS) scheduling discipline, without requiring any changes in application software or router hardware, and without adversely affecting the long flows. L 2 DCT can co-exist with TCP and works by adapting flow rates to the extent of network congestion inferred via Explicit Congestion Notification (ECN) marking, a feature widely supported by the installed router base. Though L 2 DCT is deadline unaware, our results indicate that, for typical data center traffic patterns and deadlines and over a wide range of traffic load, its deadline miss rate is consistently smaller compared to existing deadlinedriven data center transport protocols. L 2 DCT reduces the mean flow completion time by up to 50% over DCTCP and by up to 95% over TCP. In addition, it reduces the completion for 99th percentile flows by 37% over DCTCP. We present the design and analysis of L 2 DCT, evaluate its performance, and discuss an implementation built upon standard Linux protocol stack.

This paper proposes a framework for congestion control, called Binary Marking Congestion Control (BMCC) for high bandwidth-delay product networks. The basic components of BMCC are i) a packet marking scheme for obtaining high resolution congestion estimates using the existing bits available in the IP header for Explicit Congestion Notification (ECN) and ii) a set of load-dependent control laws that use these congestion estimates to achieve efficient and fair bandwidth allocations on high bandwidth-delay product networks, while maintaining a low persistent queue length and negligible packet loss rate. We present analytical models that predict and provide insights into the convergence properties of the protocol. Using extensive packet-level simulations, we assess the efficacy of BMCC and perform comparisons with several proposed schemes. BMCC outperforms VCP, MLCP, XCP, SACK+RED/ECN and in some cases RCP, in terms of average flow completion times for typical Internet flow sizes.

Many congestion control protocols use explicit feedback from the network to achieve high performance. Most of these either require more bits for feedback than are available in the IP header or incur performance limitations due to inaccurate congestion feedback. There has been recent interest in protocols which obtain high resolution estimates of congestion by combining the ECN marks of multiple packets, and using this to guide MI-AI-MD window adaptation. This paper studies the potential of such approaches, both analytically and by simulation. The evaluation focuses on a new protocol called Binary Marking Congestion Control (BMCC). It is shown that these schemes can quickly acquire unused capacity, quickly approach a fair rate distribution, and have relatively smooth sending rates, even on high bandwidth-delay product networks. This is achieved while maintaining low average queue length and negligible packet loss. Using extensive simulations, we show that BMCC outperforms XCP, VCP MLCP, CUBIC, CTCP, SACK, and in some cases RCP, in terms of average flow completion times. Suggestions are also given for the incremental deployment of BMCC.

Fake news is a growing problem in developing countries with potentially far-reaching consequences. We conduct a randomized experiment in urban Pakistan to evaluate the effectiveness of two educational interventions to counter misinformation among low-digital literacy populations. We do not find a significant effect of video-based general educational messages about misinformation. However, when such messages are augmented with personalized feedback based on individuals' past engagement with fake news, we find an improvement of 0.14 standard deviations in identifying fake news. We also find negative but insignificant effects on identifying true news, driven by female respondents. Our results suggest that educational interventions can enable information discernment but their effectiveness critically depends on how well their features and delivery are customized for the population of interest.