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P. F. Reynolds

dblp:76/4110 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
scheduling models
0.011975
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
0.011975
Scheduling Partially Ordered Tasks with Probabilistic Execution Times · SOSP 1975
Parallel and multicore computing › task scheduling
task graph scheduling
0.011975
Scheduling Partially Ordered Tasks with Probabilistic Execution Times · SOSP 1975
Parallel and multicore computing
task scheduling
0.011975
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
YearPublicationVenuePosition
1975 Scheduling Partially Ordered Tasks with Probabilistic Execution Times
abstract
The 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
SOSP2