EDBT 2026 Demo / reviewers in the wild / expert
Icel Wolf
dblp:47/6396
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1992
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Software 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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › parallel scheduling
malleable task scheduling |
0.0 | 1 | 1992 | Scheduling Parallelizable Tasks: Putting it All on the Shelf · SIGMETRICS 1992 |
Parallel and multicore computing
parallel scheduling |
0.0 | 1 | 1992 | Scheduling Parallelizable Tasks: Putting it All on the Shelf · SIGMETRICS 1992 |
Methods — techniques the papers use, named apart from their topics
resource allocation theory · 0.0combinatorial optimization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1992 | Scheduling Parallelizable Tasks: Putting it All on the ShelfabstractIn this paper we formulate the following natural multiprocessor scheduling problem: Consider a parallel system with P processors. Suppose that there are Ntasks to be scheduled on this system, and that the execution time of each task j ε {1,…,N} is a nonincreasing function tj(βj) of the number of processors βj ε {1,…,P} allotted to it. The goal is to find, for each task j, an allotment of processors βj, and, overall, a schedule assigning the tasks to the processors which minimizes the makespan, or latest task completion time. The so-called shelf strategy is commonly used for orthogonal rectangle packing, a related and classic optimization problem. The prime difference between the orthogonal rectangle problem and our own is that in our case the rectangles are, in some sense, malleable: The height of each rectangle is a nonincreasing function of its width. In this paper, we solve our multiprocessor scheduling problem exactly in the context of a shelf-based paradigm. The algorithm we give uses techniques from resource allocation theory and employs a variety of other combinatorial optimization techniques. John Turek, Joel L. Wolf, Krishna R. Pattipati, Philip S. Yu, Icel Wolf |
SIGMETRICS | 5 |