Demo Data from VLDB 2026 / reviewers in the wild / expert
P. F. Reynolds
dblp:76/4110
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1975
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
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
1 paper |
Parallel and multicore computing · 50% Performance modeling and evaluation · 25% Embedded and real-time systems · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
scheduling models |
0.0 | 1 | 1975 | Scheduling Partially Ordered Tasks with Probabilistic Execution Times · SOSP 1975 |
Embedded and real-time systems › real-time scheduling › real-time task models
stochastic execution times |
0.0 | 1 | 1975 | Scheduling Partially Ordered Tasks with Probabilistic Execution Times · SOSP 1975 |
Parallel and multicore computing › task scheduling
task graph scheduling |
0.0 | 1 | 1975 | Scheduling Partially Ordered Tasks with Probabilistic Execution Times · SOSP 1975 |
Parallel and multicore computing
task scheduling |
0.0 | 1 | 1975 | Scheduling Partially Ordered Tasks with Probabilistic Execution Times · SOSP 1975 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.0queueing analysis · 0.0level algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1975 | Scheduling Partially Ordered Tasks with Probabilistic Execution TimesabstractThe objective of this paper is to relate models of multi-tasking in which task times are known or known to be equal to models in which task times are unknown. We study bounds on completion times and the applicability of optimal deterministic schedules to probabilistic models. Level algorithms are shown to be optimal for forest precedence graphs in which task times are independent and identically distributed exponential or Erlang random variables. A time sharing system simulation shows that multi-tasking could reduce response times and that response time is insensitive to multi-tasking scheduling disciplines. K. Mani Chandy, P. F. Reynolds |
SOSP | 2 |