Online scheduling with general cost functions

Authors:
Sungjin Im;Benjamin Moseley;Kirk Pruhs
Affiliations:
University of Illinois, Urbana, IL;University of Illinois, Urbana, IL;University of Pittsburgh, Pittsburgh, PA
Venue:
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Year:
2012

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Abstract

We consider a general online scheduling problem on a single machine with the objective of minimizing Σjwjg(Fj), where wj is the weight/importance of job Jj, Fj is the flow time of the job in the schedule, and g is an arbitrary non-decreasing cost function. Numerous natural scheduling objectives are special cases of this general objective. We show that the scheduling algorithm Highest Density First (HDF) is (2+ε)-speed O(1)-competitive for all cost functions g simultaneously. We give lower bounds that show the HDF algorithm and this analysis are essentially optimal. Finally, we show scalable algorithms are achievable in some special cases.