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An Approximate Bribe Queueing Model for Bid Advising in Cloud Spot Markets

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Quantitative Evaluation of Systems (QEST 2021)

Abstract

We consider the scheduling system of a container cloud spot market where the user specifies the requested number of containers and their resource requirements, along with a bid value. Jobs are preemptively ordered based on their bid values as the available capacity, which is excess capacity made available for the spot market, may vary over time. Due to this variation, the number of allocated containers to a job may vary during its lifetime, resulting in users experiencing periods of degraded performance, potentially leading to job slowdown. We want to model and analyze such a scheduling system starting from first principles, inspired by the M/M/1 bribe queue. Thus, we introduce a simple, empirical queueing model which parametrically relates job slowdown to bid values given load and bid distribution. We demonstrate the accuracy of our approximation and parameter estimation through simulation.

This work was done while B. Ghit was an intern at the IBM T.J. Watson Research Center.

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Notes

  1. 1.

    Note that in the case of exponential service time, the preemptive-repeat and preemptive-resume cases result in similar expressions for the average response time.

  2. 2.

    Note that we define the slowdown as the ratio of two average values, and not the average of a ratio of two values. The latter alternative definition would have (1) resulted in a more complex derivation and conditional expression on the service time and, more importantly, (2) necessitated a priori knowledge of job service time, which may not be available in practice.

  3. 3.

    We will use the words bribe and bid interchangeably throughout this paper.

  4. 4.

    In practice, users may favor bidding low.

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Correspondence to Bogdan Ghiț .

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Ghiț, B., Tantawi, A. (2021). An Approximate Bribe Queueing Model for Bid Advising in Cloud Spot Markets. In: Abate, A., Marin, A. (eds) Quantitative Evaluation of Systems. QEST 2021. Lecture Notes in Computer Science(), vol 12846. Springer, Cham. https://doi.org/10.1007/978-3-030-85172-9_10

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  • DOI: https://doi.org/10.1007/978-3-030-85172-9_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-85172-9

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