Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Pragatheeswaran Angu"'
Autor:
Justin P. Rohrer, Deep Medhi, Andy Bavier, Xuan Liu, James P. G. Sterbenz, Ramkumar Cherukuri, Byrav Ramamurthy, Caterina Scoglio, Egemen K. Çetinkaya, Pragatheeswaran Angu, Cort Buffington
Publikováno v:
Computer Networks. 61:51-74
The Great Plains Environment for Network Innovation (GpENI) is an international programmable network testbed centered initially in the Midwest US with the goal to provide programmability across the entire protocol stack. In this paper, we present the
Autor:
Byrav Ramamurthy, Pragatheeswaran Angu
Publikováno v:
2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
In this paper we share our experiences of enabling dynamic circuit creation in the GpENI network. GpENI is a network research testbed in the mid-west USA involving several educational institutions. University of Nebraska-Lincoln is involved in provis
Autor:
John Sherrell, Caterina Scoglio, James P. G. Sterbenz, Don Gruenbacher, Ramkumar Cherukuri, Byrav Ramamurthy, Andrew Scott, Nidhi Tare, Tricha Anjali, Rick McMullen, Haiyang Qian, Bernhard Plattner, Gregory E. Monaco, David Hutchison, Pragatheeswaran Angu, Cort Buffington, Justin P. Rohrer, Deep Medhi
Publikováno v:
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783642178504
TRIDENTCOM
TRIDENTCOM
The Great Plains Environment for Network Innovation – GpENI is an international programmable network testbed centered on a regional optical network in the Midwest US, providing flexible infrastructure across the entire protocol stack. The goal of G
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::667dfe75f078b879974b85810692747d
https://doi.org/10.1007/978-3-642-17851-1_33
https://doi.org/10.1007/978-3-642-17851-1_33
Autor:
Pragatheeswaran Angu, Byrav Ramamurthy
Publikováno v:
GLOBECOM
Many scientific and Grid applications require high-speed circuits of guaranteed bandwidth for scheduled transfers. Offline optimization of dynamic scheduled bandwidth demands is an efficient way of finding the near-optimal solution to the bandwidth s