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Ankur Jain

Ankur Jain

Ankur Jain is a Distinguished Engineer and works in the Office of the CEO on cross-Google programs. His current areas of focus include 5G, Privacy.

Ankur was previously working on Google’s connectivity and communication products where he led the infrastructure teams running Fi, Loon, Station, RCS, Google Voice and CBRS-based shared networks and worked with some of the largest wireless operators globally in modernizing their networks. Before that he was instrumental in bringing software defined networking and disaggregation to Google’s edge network. He was one of the first engineers and later led Google’s content delivery network as it grew into the largest in the world, deployed by several hundred operators globally. He is currently on the Technical Leadership Team of Open Network Foundation bringing his experience in building and operating large-scale software-defined, automated, cloud-based networks to the open-source world.

Ankur holds a masters degree in computer science and engineering from University of Washington Seattle and a bachelors degree in the same from Indian Institute of Technology Delhi. He has a few dozen patents and conference papers filed/granted/published. His closest shot at stardom though was when he went to Los Angeles in 2013 as part of the team that collected the 65th Primetime Emmy Engineering Award for YouTube; but a couple of years later he is still happily at Google.
Authored Publications
Google Publications
Other Publications
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    Taking the Edge off with Espresso: Scale, Reliability and Programmability for Global Internet Peering
    Matthew Holliman
    Gary Baldus
    Marcus Hines
    TaeEun Kim
    Ashok Narayanan
    Victor Lin
    Colin Rice
    Brian Rogan
    Bert Tanaka
    Manish Verma
    Puneet Sood
    Mukarram Tariq
    Dzevad Trumic
    Vytautas Valancius
    Calvin Ying
    Mahesh Kallahalla
    Sigcomm (2017)
    Preview abstract We present the design of Espresso, Google’s SDN-based Internet peering edge routing infrastructure. This architecture grew out of a need to exponentially scale the Internet edge cost-effectively and to enable application-aware routing at Internet-peering scale. Espresso utilizes commodity switches and host-based routing/packet processing to implement a novel fine-grained traffic engineering capability. Overall, Espresso provides Google a scalable peering edge that is programmable, reliable, and integrated with global traffic systems. Espresso also greatly accelerated deployment of new networking features at our peering edge. Espresso has been in production for two years and serves over 22% of Google’s total traffic to the Internet. View details
    CQIC: Revisiting Cross-Layer Congestion Control f or Cellular Networks
    Hao Du
    Geoffrey M. Voelker
    Alex C. Snoeren
    Proceedings of The 16th International Workshop on Mobile Computing Systems and Applications (HotMobile), ACM (2015), pp. 45-50
    Preview abstract With the advent of high-speed cellular access and the overwhelming popularity of smartphones, a large percent of today’s Internet content is being delivered via cellular links. Due to the nature of long-range wireless signal propagation, the capacity of the last hop cellular link can vary by orders of magnitude within a short period of time (e.g., a few seconds). Unfortunately, TCP does not perform well in such fast-changing environments, potentially leading to poor spectrum utilization and high end-to-end packet delay. In this paper we revisit seminal work in cross-layer optimization the context of 4G cellular networks. Specifically, we leverage the rich physical layer information exchanged between base stations (NodeB) and mobile phones (UE) to predict the capacity of the underlying cellular link, and propose CQIC, a cross-layer congestion control design. Experiments on real cellular networks confirm that our capacity estimation method is both accurate and precise. A CQIC sender uses these capacity estimates to adjust its packet sending behavior. Our preliminary evaluation reveals that CQIC improves throughput over TCP by 1.08–2.89 × for small and medium flows. For large flows, CQIC attains throughput comparable to TCP while reducing the average RTT by 2.38–2.65x. View details
    Reducing Web Latency: the Virtue of Gentle Aggression
    Tobias Flach
    Barath Raghavan
    Shuai Hao
    Ethan Katz-Bassett
    Ramesh Govindan
    Proceedings of the ACM Conference of the Special Interest Group on Data Communication (SIGCOMM '13), ACM (2013)
    Preview abstract To serve users quickly, Web service providers build infrastructure closer to clients and use multi-stage transport connections. Although these changes reduce client-perceived round-trip times, TCP's current mechanisms fundamentally limit latency improvements. We performed a measurement study of a large Web service provider and found that, while connections with no loss complete close to the ideal latency of one round-trip time, TCP's timeout-driven recovery causes transfers with loss to take five times longer on average. In this paper, we present the design of novel loss recovery mechanisms for TCP that judiciously use redundant transmissions to minimize timeout-driven recovery. Proactive, Reactive, and Corrective are three qualitatively different, easily-deployable mechanisms that (1) proactively recover from losses, (2) recover from them as quickly as possible, and (3) reconstruct packets to mask loss. Crucially, the mechanisms are compatible both with middleboxes and with TCP's existing congestion control and loss recovery. Our large-scale experiments on Google's production network that serves billions of flows demonstrate a 23% decrease in the mean and 47% in 99th percentile latency over today's TCP. View details
    Trickle: Rate Limiting YouTube Video Streaming
    Monia Ghobadi
    Matt Mathis
    Proceedings of the USENIX Annual Technical Conference (2012), pp. 6
    Preview abstract YouTube traffic is bursty. These bursts trigger packet losses and stress router queues, causing TCP’s congestion-control algorithm to kick in. In this paper, we introduce Trickle, a server-side mechanism that uses TCP to rate limit YouTube video streaming. Trickle paces the video stream by placing an upper bound on TCP’s congestion window as a function of the streaming rate and the round-trip time. We evaluated Trickle on YouTube production data centers in Europe and India and analyzed its impact on losses, bandwidth, RTT, and video buffer under-run events. The results show that Trickle reduces the average TCP loss rate by up to 43% and the average RTT by up to 28% while maintaining the streaming rate requested by the application. View details
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