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Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms

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Zhenhua LiuAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 47, Issue 1

Pages 47 - 48

https://doi.org/10.1145/3376930.3376961

Published: 17 December 2019 Publication History

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Abstract

Several different control methods are used in practice or have been proposed to cost-effectively provision IT resources. Due to the dependency of many control methods on having accurate predictions of the future to make good provisioning decisions, there has been a great deal of literature on prediction workload demand. However, even with all of this literature on workload predictions and their utilization in control algorithms, the understanding of prediction error and how to handle it remains an important open issue and research challenge [1].

References

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[2]

Niangjun Chen, Joshua Comden, Zhenhua Liu, Anshul Gandhi, and Adam Wierman. 2016. Using predictions in online optimization: Looking forward with an eye on the past. In Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science. ACM, 193--206.

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Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proceedings of the ACM on Measurement and Analysis of Computing Systems 3, 1 (2019), 16.

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Cited By

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Yang JRen S(2021)Bandit Learning with Predicted Context: Regret Analysis and Selective Context QueryIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488896(1-10)Online publication date: 10-May-2021
https://dl.acm.org/doi/10.1109/INFOCOM42981.2021.9488896

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 47, Issue 1

June 2019

100 pages

ISSN:0163-5999

DOI:10.1145/3376930

Editor:
Zhenhua Liu
Stony Brook University

Issue’s Table of Contents

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 17 December 2019

Published in SIGMETRICS Volume 47, Issue 1

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Yang JRen S(2021)Bandit Learning with Predicted Context: Regret Analysis and Selective Context QueryIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488896(1-10)Online publication date: 10-May-2021
https://dl.acm.org/doi/10.1109/INFOCOM42981.2021.9488896

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