Scheduling parallelizable jobs online to maximize throughput
Document Type
Conference Proceeding
Publication Date
1-1-2018
Abstract
In this paper, we consider scheduling parallelizable jobs online to maximize the throughput or profit of the schedule. In particular, a set of n jobs arrive online and each job Ji arriving at time ri has an associated function pi(t) which is the profit obtained for finishing job Ji at time t+ ri. Each job can have its own arbitrary non-increasing profit function. We consider the case where each job is a parallel job that can be represented as a directed acyclic graph (DAG). We give the first non-trivial results for the profit scheduling problem for DAG jobs and show O(1)-competitive algorithms using resource augmentation.
Identifier
85045381226 (Scopus)
ISBN
[9783319774039]
Publication Title
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
External Full Text Location
https://doi.org/10.1007/978-3-319-77404-6_55
e-ISSN
16113349
ISSN
03029743
First Page
755
Last Page
776
Volume
10807 LNCS
Recommended Citation
Agrawal, Kunal; Li, Jing; Lu, Kefu; and Moseley, Benjamin, "Scheduling parallelizable jobs online to maximize throughput" (2018). Faculty Publications. 9000.
https://digitalcommons.njit.edu/fac_pubs/9000
