Nathan Lewis

dblp:62/6358 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2Security and privacy · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Cloud and datacenter computing · 100%
Computer networks
1 paper
Datacenter networks · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › virtualization
network virtualization
0.722019
PicNIC: predictable virtualized NIC · SIGCOMM 2019
Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization · NSDI 2018
Cloud and datacenter computing
performance isolation
0.412019
PicNIC: predictable virtualized NIC · SIGCOMM 2019
Cloud and datacenter computing › resource management
resource multiplexing
0.412019
PicNIC: predictable virtualized NIC · SIGCOMM 2019

Methods — techniques the papers use, named apart from their topics

weighted fair queueing · 0.4receiver-driven congestion control · 0.4admission control · 0.4
YearPublicationVenuePosition
2019 PicNIC: predictable virtualized NIC
abstract
Network virtualization stacks are the linchpins of public clouds. A key goal is to provide performance isolation so that workloads on one Virtual Machine (VM) do not adversely impact the network experience of another VM. Using data from a major public cloud provider, we systematically characterize how performance isolation can break in current virtualization stacks and find a fundamental tradeoff between isolation and resource multiplexing for efficiency. In order to provide predictable performance, we propose a new system called PicNIC that shares resources efficiently in the common case while rapidly reacting to ensure isolation. PicNIC builds on three constructs to quickly detect isolation breakdown and to enforce it when necessary: CPU-fair weighted fair queues at receivers, receiver-driven congestion control for backpressure, and sender-side admission control with shaping. Based on an extensive evaluation, we show that this combination ensures isolation for VMs at sub-millisecond timescales with negligible overhead.
Praveen Kumar 0003, Nandita Dukkipati, Nathan Lewis, Yaogong Wang, Chonggang Li, Valas Valancius, Jake Adriaens, Steve D. Gribble, Nate Foster, Amin Vahdat
SIGCOMM3
2018 Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization
Michael Dalton, David Schultz, Jacob Adriaens, Ahsan Arefin, Anshuman Gupta, Brian Fahs, Dima Rubinstein, Enrique Cauich Zermeno, Erik Rubow, James Alexander Docauer, Jesse Alpert, Jing Ai, Jon Olson, Kevin DeCabooter, Marc de Kruijf, Nan Hua, Nathan Lewis, Nikhil Kasinadhuni, Riccardo Crepaldi, Srinivas Krishnan, Subbaiah Venkata, Yossi Richter, Uday Naik, Amin Vahdat
NSDI17
2008 Using trust for key distribution and route selection in Wireless Sensor Networks
abstract
This paper presents a new approach of dynamic symmetric key distribution for encrypting the communication between two nodes in a Wireless Sensor Network. The distribution of a shared key can be performed by any sensor node and does not always require that it is performed by the base station. Each node can be selected by one of its neighbor nodes in order to distribute a pair-wise key for a communication between two nodes. The selection is based on the local computation of a trust value granted by the requesting nodes. This paper also describes a dynamic trust-based route selection mechanism that each node performs to route data to any destination.
Nathan Lewis, Noria Foukia, Donovan G. Govan
NOMS1
2008 An Efficient Reputation-Based Routing Mechanism for Wireless Sensor Networks: Testing the Impact of Mobility and Hostile Nodes
abstract
In previous works, we proposed a routing approach where nodes in a wireless sensor network (WSN) rely on trusted neighbors and neighborspsila reputation to dynamically select the best route to the destination. In this paper, we extend these previous works by adding mobile nodes to the WSN and investigating how the movements of nodes in a mobile WSN affect the success rate of routing data from a source to a destination. We also implement hostile nodes to test how our route selection and reputation mechanisms cope in their presence.
Nathan Lewis, Noria Foukia
PST1