Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Richard Lethin"'
Autor:
Julia Wei, M. Harper Langston, Pierre-David Letourneau, Matthew J. Morse, Larry Weintraub, Aimee Nogoy, Noah Amsel, Richard Lethin
Publikováno v:
2021 IEEE High Performance Extreme Computing Conference (HPEC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d04066644b31572cb0b8f37a1f485286
https://doi.org/10.1109/hpec49654.2021.9622793
https://doi.org/10.1109/hpec49654.2021.9622793
Autor:
Leandros Tassiulas, Sruthi Yellamraju, Min Yee Teh, Zhenguo Wu, Jordi Ros-Giralt, Aosong Feng, James Ezick, Noah Amsel, Keren Bergman, Yuang Jiang, Richard Lethin
Publikováno v:
SIGCOMM
This paper provides a mathematical model of data center performance based on the recently introduced Quantitative Theory of Bottleneck Structures (QTBS). Using the model, we prove that if the traffic pattern is \textit{interference-free}, there exist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aceae3089bceda0bc4ddb8a2bd398962
https://doi.org/10.1145/3452296.3472898
https://doi.org/10.1145/3452296.3472898
Publikováno v:
Future Generation Computer Systems. 96:207-215
The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::412538d46dd2fc045e4622357d98855c
https://doi.org/10.1016/j.future.2019.01.039
https://doi.org/10.1016/j.future.2019.01.039
Autor:
Alison Ryan, Brendan von Hofe, Noah Amsel, James Ezick, Richard Lethin, Sruthi Yellamraju, Jordi Ros-Giralt
Publikováno v:
INDIS@SC
The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful anal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc2761c1f5bc30f19edaa4023f958036
https://doi.org/10.1109/indis51933.2020.00012
https://doi.org/10.1109/indis51933.2020.00012
Autor:
Larry Weintraub, Mitchell Tong Harris, Pierre-David Letourneau, Julia Wei, Eric Papenhausen, Richard Lethin, M. Harper Langston, Meifeng Lin
Publikováno v:
HPEC
As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e22336bf8343637b53e79fcae5fc186f
https://doi.org/10.1109/hpec43674.2020.9286201
https://doi.org/10.1109/hpec43674.2020.9286201
Autor:
Muthu Baskaran, Brendan von Hofe, James Ezick, Thomas Henretty, Dimitri Leggas, M. Harper Langston, Richard Lethin, Grace Cimaszewski
Publikováno v:
HPEC
Large data sets can contain patterns at multiple scales (spatial, temporal, etc.). In practice, it is useful for data exploration techniques to detect patterns at each relevant scale. In this paper, we develop an approach to detect activities at mult
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58f95a68f4baf4edac0b278b4224cf01
https://doi.org/10.1109/hpec43674.2020.9286171
https://doi.org/10.1109/hpec43674.2020.9286171
Autor:
Richard Lethin, Leandros Tassiulas, Malathi Veeraraghavan, Jordi Ros-Giralt, Atul Bohara, Josie Li, Sruthi Yellamraju, Yuanlong Tan, Yuang Jiang, M. Harper Langston
Publikováno v:
SIGMETRICS (Abstracts)
In this paper, we introduce theTheory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data networks. This theoretical framework provides insights into the inherent topological properties of a network in at le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::825cba50fa582aa5e6b49af91020c061
https://doi.org/10.1145/3393691.3394204
https://doi.org/10.1145/3393691.3394204
Autor:
Richard Lethin
Publikováno v:
Find Your Path ISBN: 9780262354837
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd89c7cab0870d58d32fb1b9f43bc35f
https://doi.org/10.7551/mitpress/12391.003.0010
https://doi.org/10.7551/mitpress/12391.003.0010
Autor:
Sruthi Yellamraju, Ying Lin, Yuang Jiang, Leandros Tassiulas, Josie Li, Yuanlong Tan, Richard Lethin, Malathi Veeraraghavan, Atul Bohara, Jordi Ros-Giralt
Publikováno v:
INDIS@SC
Congestion control algorithms for data networks have been the subject of intense research for the last three decades. While most of the work has focused around the characterization of a flow's bottleneck link, understanding the interactions amongst l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65bd9e4766d3541300d445f42776e80a
https://doi.org/10.1109/indis49552.2019.00011
https://doi.org/10.1109/indis49552.2019.00011
Autor:
Christopher Coley, James Ezick, Thomas Henretty, Tai-Ching Tuan, Leslie Leonard, John Feo, William Glodek, Muthu Baskaran, Ben Parsons, Rajeev Agrawal, Richard Lethin
Publikováno v:
HPEC
This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::740ade9875de581525e206a7d6fd4564
https://doi.org/10.1109/hpec.2019.8916559
https://doi.org/10.1109/hpec.2019.8916559