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Scheduling with Predictions and the Price of Misprediction

Author Michael Mitzenmacher

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Author Details

Michael Mitzenmacher
  • School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

Funding

  • Mitzenmacher, Michael: This work was supported in part by NSF grants CCF-1563710 and CCF-1535795.

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Michael Mitzenmacher. Scheduling with Predictions and the Price of Misprediction. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 14:1-14:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.ITCS.2020.14

Abstract

In many traditional job scheduling settings, it is assumed that one knows the time it will take for a job to complete service. In such cases, strategies such as shortest job first can be used to improve performance in terms of measures such as the average time a job waits in the system. We consider the setting where the service time is not known, but is predicted by for example a machine learning algorithm. Our main result is the derivation, under natural assumptions, of formulae for the performance of several strategies for queueing systems that use predictions for service times in order to schedule jobs. As part of our analysis, we suggest the framework of the "price of misprediction," which offers a measure of the cost of using predicted information.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Queueing theory
  • Theory of computation → Online algorithms
  • Theory of computation → Scheduling algorithms
Keywords
  • Queues
  • shortest remaining processing time
  • algorithms with predictions
  • scheduling

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