VLDB 2026 Research / reviewers in the wild / expert
Travis Newhouse
dblp:65/6002
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Operating systems › resource management › process management
CPU scheduling |
0.1 | 1 | 2006 | ALPS: An Application-Level Proportional-Share Scheduler · HPDC 2006 |
Operating systems › resource management › process management › CPU scheduling
proportional share scheduling |
0.1 | 1 | 2006 | ALPS: An Application-Level Proportional-Share Scheduler · HPDC 2006 |
Distributed systems › distributed scheduling
application-level scheduling |
0.0 | 1 | 2006 | ALPS: An Application-Level Proportional-Share Scheduler · HPDC 2006 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 SchedulerabstractALPS 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 |
HPDC | 1 |
| 2005 | Java active extensions: Scalable middleware for performance-isolated remote execution
Travis Newhouse, Joseph Pasquale |
Comput. Commun. | 1 |