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Travis Newhouse

dblp:65/6002 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2007
—ORCID · none

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

Systems, architecture and hardware · 2 · 2 first-authorComputer networks · 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.

Software engineering, system software, and programming languages
1 paper
Operating systems · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

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

TopicWeightPapersLastEvidence papers
Operating systems › resource management › process management
CPU scheduling
0.112006
ALPS: An Application-Level Proportional-Share Scheduler · HPDC 2006
Operating systems › resource management › process management › CPU scheduling
proportional share scheduling
0.112006
ALPS: An Application-Level Proportional-Share Scheduler · HPDC 2006
Distributed systems › distributed scheduling
application-level scheduling
0.012006
ALPS: An Application-Level Proportional-Share Scheduler · HPDC 2006
YearPublicationVenuePosition
2007 Achieving Efficiency and Accuracy in the ALPS Application-level Proportional-share Scheduler
Travis Newhouse, Joseph Pasquale
J. Grid Comput.1
2006 ALPS: An Application-Level Proportional-Share Scheduler
abstract
ALPS is a per-application user-level proportional-share scheduler that operates with tow overhead and without any special kernel support. ALPS is useful to a range of applications, including scientific applications that need to control the CPU apportionment to the processes they create, to Web servers that need to limit the proportion of available CPU time given to spawned processes that service Web requests, and to middleware that supports multiple execution environments that are to run at different rates. ALPS works by minimally sampling the progress of processes under its control, and making simple predictions for when it should selectively pause and resume the processes. We present the algorithm, a UNIX-based implementation, and a performance evaluation. Our results show that the ALPS approach is practical; we can achieve good accuracy (under 5% error), and low overhead (under 1% of CPU), despite user-level operation
Travis Newhouse, Joseph Pasquale
HPDC1
2005 Java active extensions: Scalable middleware for performance-isolated remote execution
Travis Newhouse, Joseph Pasquale
Comput. Commun.1