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Cloud Workload Prediction by Means of Simulations

Authors:

Gabor Kecskemeti,

Attila Kertesz,

Zsolt NemethAuthors Info & Claims

CF'17: Proceedings of the Computing Frontiers Conference

Pages 279 - 282

https://doi.org/10.1145/3075564.3075589

Published: 15 May 2017 Publication History

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Abstract

Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application.

References

[1]

P. Horn. Autonomic Computing: IBM's Perspective on the State of Information Technology. IBM TJ Watson Labs., October 2001.

Google Scholar

[2]

Rodrigo N Calheiros, Enayat Masoumi, Rajiv Ranjan, and Rajkumar Buyya. Workload prediction using ARIMA model and its impact on cloud applications' QoS. IEEE Transactions on Cloud Computing, 3(4):449--458, 2015.

Digital Library

Google Scholar

[3]

Deborah Magalhaes, Rodrigo N. Calheiros, Rajkumar Buyya, and Danielo G. Gomes. Workload modeling for resource usage analysis and simulation in cloud computing. Comput. Electr. Eng., 47(C):69--81, October 2015.

Digital Library

Google Scholar

[4]

E. Caron, F. Desprez, and A. Muresan. Forecasting for grid and cloud computing on-demand resources based on pattern matching. In Cloud Computing Technology and Science (CloudCom), 2010 IEEE Second International Conference on, pages 456--463, Nov 2010.

Digital Library

Google Scholar

[5]

Gábor Bacsó, Adám Visegrádi, Attila Kertesz, and Zsolt Németh. On efficiency of multi-job grid allocation based on statistical trace data. Journal of Grid Computing, 12(1):169--186, 2013.

Digital Library

Google Scholar

[6]

A. Kertesz, F. Otvos, and P. Kacsuk. A case study for biochemical application porting in european grids and clouds. Special Issue on Distributed, Parallel, and GPU-accelerated Approaches to Computational Biology, Concurrency and Computation: Practice and Experience, August 2013.

Digital Library

Google Scholar

[7]

Gabor Kecskemeti. DISSECT-CF: a simulator to foster energy-aware scheduling in infrastructure clouds. Simulation Modelling Practice and Theory, 58P2:188--218, November 2015.

Crossref

Google Scholar

[8]

Alexandru Iosup, Hui Li, Mathieu Jan, Shanny Anoep, Catalin Dumitrescu, Lex Wolters, and D. H. J. Epema. The grid workloads archive. Future Generation Comp. Syst., 24(7):672--686, 2008.

Digital Library

Google Scholar

Cited By

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Guo JWang JWang JDong AZhang B(2021)A Average Response Time Prediction Method For Seasonal Non-Stationary Concurrency Based On Improved RBF Algorithm2021 33rd Chinese Control and Decision Conference (CCDC)10.1109/CCDC52312.2021.9602449(586-591)Online publication date: 22-May-2021
https://doi.org/10.1109/CCDC52312.2021.9602449
Mazidi AGolsorkhtabaramiri MYadollahzadeh Tabari M(2020)An autonomic risk‐ and penalty‐aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioningInternational Journal of Communication Systems10.1002/dac.433433:7Online publication date: 7-Feb-2020
https://doi.org/10.1002/dac.4334
Chen JWang Y(2019)A Hybrid Method for Short-Term Host Utilization Prediction in Cloud ComputingJournal of Electrical and Computer Engineering10.1155/2019/27823492019Online publication date: 14-Mar-2019
https://dl.acm.org/doi/10.1155/2019/2782349
Show More Cited By

Index Terms

Cloud Workload Prediction by Means of Simulations
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing

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Published In

CF'17: Proceedings of the Computing Frontiers Conference

May 2017

450 pages

ISBN:9781450344876

DOI:10.1145/3075564

General Chair:
Roberto Giorgi
University of Siena, IT
,
Program Chairs:
Michela Becchi
North Carolina State University
,
Francesca Palumbo
University of Sassari, IT

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 15 May 2017

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CF '17: Computing Frontiers Conference

May 15 - 17, 2017

Siena, Italy

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CF'17 Paper Acceptance Rate 43 of 87 submissions, 49%;

Overall Acceptance Rate 273 of 785 submissions, 35%

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22nd ACM International Conference on Computing Frontiers

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Guo JWang JWang JDong AZhang B(2021)A Average Response Time Prediction Method For Seasonal Non-Stationary Concurrency Based On Improved RBF Algorithm2021 33rd Chinese Control and Decision Conference (CCDC)10.1109/CCDC52312.2021.9602449(586-591)Online publication date: 22-May-2021
https://doi.org/10.1109/CCDC52312.2021.9602449
Mazidi AGolsorkhtabaramiri MYadollahzadeh Tabari M(2020)An autonomic risk‐ and penalty‐aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioningInternational Journal of Communication Systems10.1002/dac.433433:7Online publication date: 7-Feb-2020
https://doi.org/10.1002/dac.4334
Chen JWang Y(2019)A Hybrid Method for Short-Term Host Utilization Prediction in Cloud ComputingJournal of Electrical and Computer Engineering10.1155/2019/27823492019Online publication date: 14-Mar-2019
https://dl.acm.org/doi/10.1155/2019/2782349
Kecskemeti GMarkus AKertesz A(2019)Towards Pricing-Aware Consolidation Methods for Cloud DatacentersCloud Computing and Services Science10.1007/978-3-030-29193-8_8(152-167)Online publication date: 10-Aug-2019
https://doi.org/10.1007/978-3-030-29193-8_8

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esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

Workload Prediction of Cloud Workflow Based on Graph Neural Network

Cloud workload prediction based on workflow execution time discrepancies

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