Flow Time Scheduling with Uncertain Processing Time

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=X8ejQjeskFM


Duration: 52:11
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Yossi Azar (Tel-Aviv University)
https://simons.berkeley.edu/talks/flow-time-scheduling-uncertain-processing-time
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

We consider the problem of online scheduling on a single machine to minimize unweighted and weighted flow time. The existing algorithms for these problems require exact knowledge of the processing time of each job. This assumption is crucial, as even a slight perturbation of the processing time would lead to polynomial competitive ratio. However, this assumption very rarely holds in real-life scenarios. We present a competitive algorithm (the competitive ratio is a function of the distortion) for unweighted flow time that do not require knowledge of the distortion in advance. For the weighted flow time we present competitive algorithms but, in this case, we need to know (an upper bound on) the distortion in advance. This is joint work with Stefano Leonardi and Noam Touitou based on papers appear in STOC 21 and SODA 2022.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Quantifying Uncertainty: Stochastic Adversarial and Beyond
Yossi Azar