Founded in 2002, our laboratory conducts research on the design and implementation of a wide range of networked computing systems.
Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AUTOCAST1 enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AUTOCAST can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.
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.
While prior work has explored many proposed datacenter designs, only two designs, Clos-based and expander-based, are generally considered practical because they can scale using commodity switching chips. Prior work has used two different metrics, bisection bandwidth and throughput, for evaluating these topologies at scale. Little is known, theoretically or practically, how these metrics relate to each other. Exploiting characteristics of these topologies, we prove an upper bound on their throughput, then show that this upper bound better estimates worst-case throughput than all previously proposed throughput estimators and scales better than most of them. Using this upper bound, we show that for expander-based topologies, unlike Clos, beyond a certain size of the network, no topology can have full throughput, even if it has full bisection bandwidth; in fact, even relatively small expander-based topologies fail to achieve full throughput. We conclude by showing that using throughput to evaluate datacenter performance instead of bisection bandwidth can alter conclusions in prior work about datacenter cost, manageability, and reliability.
For decades, Internet protocols have been specified using natural language. Given the ambiguity inherent in such text, it is not surprising that protocol implementations have long exhibited bugs. In this paper, we apply natural language processing (NLP) to effect semi-automated generation of protocol implementations from specification text. Our system, Sage, can uncover ambiguous or under-specified sentences in specifications; once these are clarified by the author of the protocol specification, Sage can generate protocol code automatically.Using Sage, we discover 5 instances of ambiguity and 6 instances of under-specification in the ICMP RFC; after fixing these, Sage is able to automatically generate code that interoperates perfectly with Linux implementations. We show that Sage generalizes to sections of BFD, IGMP, and NTP and identify additional conceptual components that Sage needs to support to generalize to complete, complex protocols like BGP and TCP.
Nov 7, 2022
Our Wisden paper receives the Test of Time Award at Sensys!
Sep 5, 2022
Quadrant accepted to SoCC 2022
May 4, 2022
Sarah Cooney accepts position at Villanova University. Congrats!
April 20, 2022
Fawad Ahmad accepts position at Rochester Institute of Technology. Congrats!
April 1, 2022
CloudCluster accepted to NSDI 2022
March 20, 2022
Autocast accepted to MobiSys 2022
December 3, 2021
Optimal Oblivious Routing accepted to Infocom 2022
December 1 2021
Mingyang Zhang joins Google NetInfra Team! Congrats!