Resolution Matters : Revisiting Prediction-Based Job Co-location in Public Clouds

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC 2022)
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages163-166
ISBN (electronic)978-1-6654-6087-3
Publication statusPublished - Dec 2022

Publication series

NameProceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022

Conference

Title15th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2022
PlaceUnited States
CityVancouver
Period6 - 9 December 2022

Abstract

Overall resource utilization in public cloud data centers remains very low. To increase the efficiency of these data centers, low priority batch jobs are often co-located on the same machines as latency-sensitive jobs. Existing methodologies have used machine learning to predict the amount of resources that should be reserved for these jobs to maintain acceptable latency. However, these methodologies overlook the impact of measurement granularity on usage prediction and scheduling performance. When batch jobs have long durations, coarsegrained data can be used to make the prediction problem less challenging, but the resulting predictions may degrade scheduling performance. In this paper, we investigate the impact of measurement granularity on scheduler performance using extensive trace-driven simulation and job data generated from the Alibaba cluster trace. © 2022 IEEE.

Research Area(s)

  • Cloud computing, Job scheduling, Resource usage prediction

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Resolution Matters: Revisiting Prediction-Based Job Co-location in Public Clouds. / Kur, Justin; Chen, Jingshu; Xue, Ji et al.
Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC 2022). Institute of Electrical and Electronics Engineers, Inc., 2022. p. 163-166 (Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review