A General Framework for Handling Commitment in Online Throughput Maximization

Lin Chen, Franziska Eberle, Nicole Megow, Kevin Schewior, Cliff Stein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We study a fundamental online job admission problem where jobs with deadlines arrive online over time at their release dates, and the task is to determine a preemptive single-server schedule which maximizes the number of jobs that complete on time. To circumvent known impossibility results, we make a standard slackness assumption by which the feasible time window for scheduling a job is at least times its processing time, for some We quantify the impact that different provider commitment requirements have on the performance of online algorithms. Our main contribution is one universal algorithmic framework for online job admission both with and without commitments. Without commitment, our algorithm with a competitive ratio of is the best possible (deterministic) for this problem. For commitment models, we give the first non-trivial performance bounds. If the commitment decisions must be made before a job’s slack becomes less than a fraction of its size, we prove a competitive ratio of for When a scheduler must commit upon starting a job, our bound is Finally, we observe that for scheduling with commitment the restriction to the “unweighted” throughput model is essential; if jobs have individual weights, we rule out competitive deterministic algorithms.

Original languageEnglish
Title of host publicationInteger Programming and Combinatorial Optimization - 20th International Conference, IPCO 2019, Proceedings
EditorsAndrea Lodi, Viswanath Nagarajan
PublisherSpringer-Verlag
Pages141-154
Number of pages14
ISBN (Print)9783030179526
DOIs
StatePublished - 2019
Event20th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2019 - Ann Arbor, United States
Duration: May 22 2019May 24 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11480 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2019
CountryUnited States
CityAnn Arbor
Period05/22/1905/24/19

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