Windows scheduling of arbitrary-length jobs on multiple machines

The generalized windows scheduling problem for n jobs on multiple machines is defined as follows: Given is a sequence, I =〈( w 1 , ℓ 1 ),( w 2 , ℓ 2 ),…,( w n , ℓ n )〉 of n pairs of positive integers that are associated with the jobs 1,2,…, n , respectively. The processing length of job i is ℓ i slo...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of scheduling 2012-04, Vol.15 (2), p.141-155
Hauptverfasser: Bar-Noy, Amotz, Ladner, Richard E., Tamir, Tami, VanDeGrift, Tammy
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The generalized windows scheduling problem for n jobs on multiple machines is defined as follows: Given is a sequence, I =〈( w 1 , ℓ 1 ),( w 2 , ℓ 2 ),…,( w n , ℓ n )〉 of n pairs of positive integers that are associated with the jobs 1,2,…, n , respectively. The processing length of job i is ℓ i slots where a slot is the processing time of one unit of length. The goal is to repeatedly and non-preemptively schedule all the jobs on the fewest possible machines such that the gap (window) between two consecutive beginnings of executions of job i is at most w i slots. This problem arises in push broadcast systems in which data are transmitted on multiple channels. The problem is NP-hard even for unit-length jobs and a (1+ ε )-approximation algorithm is known for this case by approximating the natural lower bound . The techniques used for approximating unit-length jobs cannot be extended for arbitrary-length jobs mainly because the optimal number of machines might be arbitrarily larger than the generalized lower bound . The main result of this paper is an 8-approximation algorithm for the WS problem with arbitrary lengths using new methods, different from those used for the unit-length case. The paper also presents another algorithm that uses 2(1+ ε ) W ( I )+log w max machines and a greedy algorithm that is based on a new tree representation of schedules. The greedy algorithm is optimal for some special cases, and computational experiments show that it performs very well in practice.
ISSN:1094-6136
1099-1425
DOI:10.1007/s10951-011-0263-8