We consider the problem of scheduling jobs to minimize the maximum weighted flow-time on a set of related machines. When jobs can be preempted this problem is well-understood; for example, there exists a constant competitive algorithm using speed augmentation. When jobs must be scheduled non-preemptively, only hardness results are known. In this paper, we present the first online guarantees for the non-preemptive variant. We present the first constant competitive algorithm for minimizing the maximum weighted flow-time on related machines by relaxing the problem and assuming that the online algorithm can reject a small fraction of the total weight of jobs. This is essentially the best result possible given the strong lower bounds on the non-preemptive problem without rejection.
@InProceedings{lucarelli_et_al:LIPIcs.FSTTCS.2019.24, author = {Lucarelli, Giorgio and Moseley, Benjamin and Thang, Nguyen Kim and Srivastav, Abhinav and Trystram, Denis}, title = {{Online Non-Preemptive Scheduling to Minimize Maximum Weighted Flow-Time on Related Machines}}, booktitle = {39th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2019)}, pages = {24:1--24:12}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-131-3}, ISSN = {1868-8969}, year = {2019}, volume = {150}, editor = {Chattopadhyay, Arkadev and Gastin, Paul}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2019.24}, URN = {urn:nbn:de:0030-drops-115867}, doi = {10.4230/LIPIcs.FSTTCS.2019.24}, annote = {Keywords: Online Algorithms, Scheduling, Resource Augmentation} }
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