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Christophe Diot

Christophe Diot

Christophe Diot received a Ph.D. degree in Computer Science from INP Grenoble in 1991. Diot pioneered diffserv, single source multicast, epidemic communication, peer-to-peer online games, and most importantly Internet measurements. After INRIA (years 93-98 in Sophia Antipolis), Diot spent his career in industry, building R&D labs at Sprint (Bay area), INTEL Research (Cambridge), and Technicolor (Paris and Palo Alto). He was the Chief Scientist at Technicolor between 2009 and 2015. He helped launch Safran Analytics as their CTO before joining GOOGLE in june 2018 as Principal Engineer in the Network Architecture team. At GOOGLE, Diot deals with telemetry at scale in the cloud infrastructure. Since January 2020, Diot is the Technical Lead of the Network Analytics team in the Google Global Networks organization. Diot has around 40 patents and more than 300 publications in major conferences and journals. He is an ACM fellow.
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    CloudCluster: Unearthing the Functional Structure of a Cloud Service
    weiwu pang
    Sourav Panda
    Jehangir Amjad
    Ramesh Govindan
    NSDI 2022, USENIX (2022)
    Preview abstract In their quest to provide customers with good tools to manage cloud services, cloud providers are hampered by having very little visibility into cloud service functionality; a provider often only knows where VMs of a service are placed, how the virtual networks are configured, how VMs are provisioned, and how VMs communicate with each other. In this paper, we show that, using the VM-to-VM traffic matrix, we can unearth the functional structure of a cloud service and use it to aid cloud service management. Leveraging the observation that cloud services use well-known design patterns for scaling (e.g., replication, communication locality), we show that clustering the VM-to-VM traffic matrix yields the functional structure of the cloud service. Our clustering algorithm, CloudCluster, must overcome challenges imposed by scale (cloud services contain tens of thousands of VMs) and must be robust to orders-of-magnitude variability in traffic volume and measurement noise. To do this, CloudCluster uses a novel combination of feature scaling, dimensionality reduction, and hierarchical clustering to achieve clustering with over 92% homogeneity and completeness. We show that CloudCluster can be used to explore opportunities to reduce cost for customers, identify anomalous traffic and potential misconfigurations. View details
    Optimal Probing with Statistical Guarantees for Network Monitoring at Scale
    Branislav Kveton
    Jehangir Amjad
    Dimitris Konomis
    Augustin Soule
    Shawn Yang
    Computer Communication, vol. 192 (2022), pp. 119-131 (to appear)
    Preview abstract Monitoring large-scale cloud networks is a complex task because their scale is prohibitively large, monitoring budgets are limited, network topologies are not entirely regular and the estimates produced are a function of traffic patterns. In this work, we take a statistical approach to estimating a network metric, such as the latency of a set of paths, with guarantees on the estimation error. We aim to do so in an intelligent and scalable manner, without observing all existing traffic, and minimizing the estimation error at a fixed probing budget per unit of time. Our proposed algorithms produce a distribution of probes/samples across network paths which can be used in conjunction with existing probers (or samplers). These algorithms are based on A- and E-optimal experimental designs in statistics, which guarantee a bounded estimation error for any monitoring budget. Unfortunately, these designs are too computationally intensive to be used in production at scale. We propose a scalable and near-optimal approximate implementations based on the Frank-Wolfe algorithm. We validate our approaches with two metrics (latency and loss) in simulations on real network topologies, and also using a production probing system in a real cloud network. We show major gains in reducing the probing budget compared to both production and academic baselines, while maintaining low errors in estimates, even with very low probing budgets. View details
    M-Lab: User initiated Internet data for the research community
    Lai Yi Ohlsen
    Matt Mathis
    ACM SIGCOMM Computer Communication Review (2022) (to appear)
    Preview abstract Measurement Lab (M-Lab) is an open, distributed server platform on which researchers can deploy measurement tools. Its mission is to measure the Internet, save the data and make it universally accessible and useful. This paper serves as an update on the M-Lab platform 10+ years after its initial introduction to the research community. This paper details the current state of the M-Lab distributed platform, highlights existing measurements/data available on the platform, and describes opportunities for further engagement between the networking research community and the platform. View details
    Preview abstract We (Google's networking teams) would like to increase our collaborations with academic researchers related to data-driven networking research. There are some significant constraints on our ability to directly share data, and in case not everyone in the community understands these, this document provides a brief summary. There are some models which can work (primarily, interns and visiting scientists). We describe some specific areas where we would welcome proposals to work within those models View details
    Classification of load balancing in the Internet
    Darryl Veitch
    Italo Cunha
    rafael almeida
    renata cruz teixeira
    Proceedings of IEEE INFOCOM, IEEE, Beijing, China (2020)
    Preview abstract Abstract—Recent advances in programmable data planes, software-defined networking, and the adoption of IPv6, support novel, more complex load balancing strategies. We introduce the Multipath Classification Algorithm (MCA), a probing algorithm that extends traceroute to identify and classify load balancing in Internet routes. MCA extends existing formalism and techniques to consider that load balancers may use arbitrary combinations of bits in the packet header for load balancing. We propose optimizations to reduce probing cost that are applicable to MCA and existing load balancing measurement techniques. Through large-scale measurement campaigns, we characterize and study the evolution of load balancing on the IPv4 and IPv6 Internet with multiple transport protocols. Our results show that load balancing is more prevalent and that load balancing strategies are more mature than previous characterizations have found. View details
    Preview abstract When I embarked on the CCR adventure 15 years ago I did not expect it to be so exciting, fruitful, and life changing. I am describing here our motivation and approach, our successes and failures, withI hope a perceptible sense of humor. View details
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