Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161047
Title: Interval job scheduling with machine launch cost
Authors: Ren, Runtian
Zhu, Yuqing
Li, Chuanyou
Tang, Xueyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Ren, R., Zhu, Y., Li, C. & Tang, X. (2020). Interval job scheduling with machine launch cost. IEEE Transactions On Parallel and Distributed Systems, 31(12), 2776-2788. https://dx.doi.org/10.1109/TPDS.2020.3002786
Project: 2019-T1-002-042
Journal: IEEE Transactions on Parallel and Distributed Systems
Abstract: We study an interval job scheduling problem in distributed systems. We are given a set of interval jobs, with each job specified by a size, an arrival time and a processing length. Once a job arrives, it must be placed on a machine immediately and run for a period of its processing length without interruption. The homogeneous machines to run jobs have the same capacity limits such that at any time, the total size of the jobs running on any machine cannot exceed its capacity. Launching each machine incurs a fixed cost. After launch, a machine is charged a constant cost per time unit until it is terminated. The problem targets to minimize the total cost incurred by the machines for processing the given set of interval jobs. We focus on the algorithmic aspects of the problem in this article. For the special case where all the jobs have a unit size equal to the machine capacity, we propose an optimal offline algorithm and an optimal 2-competitive online algorithm. For the general case where jobs can have arbitrary sizes, we establish a non-trivial lower bound on the optimal solution. Based on this lower bound, we propose a 5-approximation algorithm in the offline setting. In the non-clairvoyant online setting, we design a O(μ)-competitive Modified First-Fit algorithm which is near optimal (μ is the max/min job processing length ratio). In the clairvoyant online setting, we propose an asymptotically optimal O(logμ)-competitive algorithm based on our Modified First-Fit strategy.
URI: https://hdl.handle.net/10356/161047
ISSN: 1045-9219
DOI: 10.1109/TPDS.2020.3002786
Schools: School of Computer Science and Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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