Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme

İlksen Çaǧlar; D. Turgay Altilar

doi:10.1109/UBMK.2017.8093548

Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme

Translated title of the contribution: Cloud work load prediction through different models based on time-series

İlksen Çaǧlar, D. Turgay Altilar

Department of Computer Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Scheduling of computational load and actual processing is an important problem to be considered from the perspectives of time and consumed energy for execution in the scale of data centers. In this paper, time-series analysis of the arrivals of the workloads have been done by applying auto regression (AR), moving average (MA), auto regression and moving average (ARMA), and Holt-Winters approaches. Performances of the four methods was evaluated and compared for Google workload logs that is publicly available in the Internet.

Translated title of the contribution	Cloud work load prediction through different models based on time-series
Original language	Turkish
Title of host publication	2nd International Conference on Computer Science and Engineering, UBMK 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	856-860
Number of pages	5
ISBN (Electronic)	9781538609309
DOIs	https://doi.org/10.1109/UBMK.2017.8093548
Publication status	Published - 31 Oct 2017
Event	2nd International Conference on Computer Science and Engineering, UBMK 2017 - Antalya, Turkey Duration: 5 Oct 2017 → 8 Oct 2017

Publication series

Name	2nd International Conference on Computer Science and Engineering, UBMK 2017

Conference

Conference	2nd International Conference on Computer Science and Engineering, UBMK 2017
Country/Territory	Turkey
City	Antalya
Period	5/10/17 → 8/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Access to Document

10.1109/UBMK.2017.8093548

Cite this

Çaǧlar, İ., & Altilar, D. T. (2017). Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme. In 2nd International Conference on Computer Science and Engineering, UBMK 2017 (pp. 856-860). Article 8093548 (2nd International Conference on Computer Science and Engineering, UBMK 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UBMK.2017.8093548

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abstract = "Scheduling of computational load and actual processing is an important problem to be considered from the perspectives of time and consumed energy for execution in the scale of data centers. In this paper, time-series analysis of the arrivals of the workloads have been done by applying auto regression (AR), moving average (MA), auto regression and moving average (ARMA), and Holt-Winters approaches. Performances of the four methods was evaluated and compared for Google workload logs that is publicly available in the Internet.",

keywords = "Cloud, Computational loads, Data centers, Holt-winters, Regression, Time-series analysis",

author = "İlksen {\c C}aǧlar and Altilar, {D. Turgay}",

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Çaǧlar, İ & Altilar, DT 2017, Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme. in 2nd International Conference on Computer Science and Engineering, UBMK 2017., 8093548, 2nd International Conference on Computer Science and Engineering, UBMK 2017, Institute of Electrical and Electronics Engineers Inc., pp. 856-860, 2nd International Conference on Computer Science and Engineering, UBMK 2017, Antalya, Turkey, 5/10/17. https://doi.org/10.1109/UBMK.2017.8093548

Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme. / Çaǧlar, İlksen; Altilar, D. Turgay.
2nd International Conference on Computer Science and Engineering, UBMK 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 856-860 8093548 (2nd International Conference on Computer Science and Engineering, UBMK 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Çaǧlar, İlksen

AU - Altilar, D. Turgay

PY - 2017/10/31

Y1 - 2017/10/31

N2 - Scheduling of computational load and actual processing is an important problem to be considered from the perspectives of time and consumed energy for execution in the scale of data centers. In this paper, time-series analysis of the arrivals of the workloads have been done by applying auto regression (AR), moving average (MA), auto regression and moving average (ARMA), and Holt-Winters approaches. Performances of the four methods was evaluated and compared for Google workload logs that is publicly available in the Internet.

AB - Scheduling of computational load and actual processing is an important problem to be considered from the perspectives of time and consumed energy for execution in the scale of data centers. In this paper, time-series analysis of the arrivals of the workloads have been done by applying auto regression (AR), moving average (MA), auto regression and moving average (ARMA), and Holt-Winters approaches. Performances of the four methods was evaluated and compared for Google workload logs that is publicly available in the Internet.

KW - Cloud

KW - Computational loads

KW - Data centers

KW - Holt-winters

KW - Regression

KW - Time-series analysis

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M3 - Konferans katkısı

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T3 - 2nd International Conference on Computer Science and Engineering, UBMK 2017

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Çaǧlar İ, Altilar DT. Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme. In 2nd International Conference on Computer Science and Engineering, UBMK 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 856-860. 8093548. (2nd International Conference on Computer Science and Engineering, UBMK 2017). doi: 10.1109/UBMK.2017.8093548

Bulut Uygulamalari için Farkli Zaman Serisi Modelleriyle İş Yükü Tahminleme

Abstract

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