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Selecting the best VM across multiple public clouds: a data-driven performance modeling approach

Authors:

Neeraja J. Yadwadkar,

Bharath Hariharan,

Joseph E. Gonzalez,

Randy H. KatzAuthors Info & Claims

SoCC '17: Proceedings of the 2017 Symposium on Cloud Computing

Pages 452 - 465

https://doi.org/10.1145/3127479.3131614

Published: 24 September 2017 Publication History

Abstract

Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.

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Cited By

Mohammad RGopalakrishnan SPattabiraman K(2024)Co-Approximator: Enabling Performance Prediction in Colocated Applications.ACM Transactions on Embedded Computing Systems10.1145/367718024:1(1-28)Online publication date: 25-Jul-2024
https://dl.acm.org/doi/10.1145/3677180
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Selecting the best VM across multiple public clouds: a data-driven performance modeling approach

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cover image ACM Conferences

SoCC '17: Proceedings of the 2017 Symposium on Cloud Computing

September 2017

672 pages

ISBN:9781450350280

DOI:10.1145/3127479

Copyright © 2017 ACM.

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Published: 24 September 2017

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SoCC '17: ACM Symposium on Cloud Computing

September 24 - 27, 2017

California, Santa Clara

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Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

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https://dl.acm.org/doi/10.1145/3677180
Casimiro MSoares DGarlan DRodrigues LRomano P(2024)Self-Adapting Machine Learning-based Systems via a Probabilistic Model Checking FrameworkACM Transactions on Autonomous and Adaptive Systems10.1145/3648682Online publication date: 7-Mar-2024
https://doi.org/10.1145/3648682
Sachidananda VSivaraman A(2024)Erlang: Application-Aware Autoscaling for Cloud MicroservicesProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650084(888-923)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650084
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Wang WLiu LYan Z(2024)Proactive Auto-Scaling for Delay-Sensitive IoT Applications Over Edge CloudsIEEE Internet of Things Journal10.1109/JIOT.2023.332454611:6(9536-9546)Online publication date: 15-Mar-2024
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