Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32329
Title: Online Scheduling of Jobs with Fixed Start Times on Related Machines
Authors: Epstein, Leah
Jeż, Łukasz
Sgall, Jiří
Van Stee, Rob
First Published: 1-Oct-2014
Publisher: Springer Verlag
Citation: Algorithmica (2016) 74:156–176
Abstract: We consider online preemptive scheduling of jobs with fixed starting times revealed at those times on m uniformly related machines, with the goal of maximizing the total weight of completed jobs. Every job has a size and a weight associated with it. A newly released job must be either assigned to start running immediately on a machine or otherwise it is dropped. It is also possible to drop an already scheduled job, but only completed jobs contribute their weights to the profit of the algorithm. In the most general setting, no algorithm has bounded competitive ratio, and we consider a number of standard variants. We give a full classification of the variants into cases which admit constant competitive ratio (weighted and unweighted unit jobs, and Cbenevolent instances, which is a wide class of instances containing proportional-weight jobs), and cases which admit only a linear competitive ratio (unweighted jobs and D-benevolent instances). In particular, we give a lower bound of m on the competitive ratio for scheduling unit weight jobs with varying sizes, which is tight. For unit size and weight we show that a natural greedy algorithm is 4/3-competitive and optimal on m = 2 machines, while for large m, its competitive ratio is between 1.56 and 2. Furthermore, no algorithm is better than 1.5-competitive.
DOI Link: 10.1007/s00453-014-9940-2
ISSN: 0178-4617
eISSN: 1432-0541
Links: http://link.springer.com/article/10.1007/s00453-014-9940-2
http://hdl.handle.net/2381/32329
Version: Publisher version
Status: Peer-reviewed
Type: Journal Article
Rights: © The Author(s) 2014. This article is published with open access at Springerlink.com
Appears in Collections:Published Articles, Dept. of Computer Science

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