Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds

Sena Seneviratne; Sanjeeva Witharana; Adel N. Toosi

doi:10.1109/ICASET.2019.8714535

Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds

Sena Seneviratne, Sanjeeva Witharana, Adel N. Toosi

Department of Software Systems & Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

7 Citations (Scopus)

Abstract

As the grid is loosely coupled congregation of geographically distributed and heterogeneous resources, the efficient scheduling of their resources require the support of a sound Performance Prediction System (PPS). The performance prediction of resources is helpful for both Resource Management Systems and users to make optimized resource usage decisions. In this paper, it is discussed about the ways of several Machine Learning (ML) PPSs that span over several grid resources in several dimensions. Taxonomy is used to categorize and identify approaches which are followed in the implementation of the existing PPSs for grids and for their adaptation in the clouds. A test framework is proposed to evaluate their capability of serving in the clouds environment.

Original language	English
Title of host publication	2019 Advances in Science and Engineering Technology International Conferences (ASET 2019)
Editors	Haris M. Khalid
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	9781538682715
ISBN (Print)	9781538682722
DOIs	https://doi.org/10.1109/ICASET.2019.8714535
Publication status	Published - 2019
Event	2019 Advances in Science and Engineering Technology International Conferences - Dubai, United Arab Emirates Duration: 26 Mar 2019 → 28 Mar 2019 http://www.aset.hct.ac.ae/aset2019/

Conference

Conference	2019 Advances in Science and Engineering Technology International Conferences
Abbreviated title	ASET 2019
Country/Territory	United Arab Emirates
City	Dubai
Period	26/03/19 → 28/03/19
Internet address	http://www.aset.hct.ac.ae/aset2019/

Keywords

clouds
grids
prediction
Resources
Taxonomy

Access to Document

10.1109/ICASET.2019.8714535

Cite this

Seneviratne, S., Witharana, S., & Toosi, A. N. (2019). Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds. In H. M. Khalid (Ed.), 2019 Advances in Science and Engineering Technology International Conferences (ASET 2019) [8714535] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASET.2019.8714535

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abstract = "As the grid is loosely coupled congregation of geographically distributed and heterogeneous resources, the efficient scheduling of their resources require the support of a sound Performance Prediction System (PPS). The performance prediction of resources is helpful for both Resource Management Systems and users to make optimized resource usage decisions. In this paper, it is discussed about the ways of several Machine Learning (ML) PPSs that span over several grid resources in several dimensions. Taxonomy is used to categorize and identify approaches which are followed in the implementation of the existing PPSs for grids and for their adaptation in the clouds. A test framework is proposed to evaluate their capability of serving in the clouds environment.",

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address = "United States of America",

note = "2019 Advances in Science and Engineering Technology International Conferences, ASET 2019 ; Conference date: 26-03-2019 Through 28-03-2019",

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Seneviratne, S, Witharana, S & Toosi, AN 2019, Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds. in H M. Khalid (ed.), 2019 Advances in Science and Engineering Technology International Conferences (ASET 2019)., 8714535, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, 2019 Advances in Science and Engineering Technology International Conferences, Dubai, United Arab Emirates, 26/03/19. https://doi.org/10.1109/ICASET.2019.8714535

Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds. / Seneviratne, Sena; Witharana, Sanjeeva; Toosi, Adel N.

2019 Advances in Science and Engineering Technology International Conferences (ASET 2019). ed. / Haris M. Khalid. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. 8714535.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AU - Witharana, Sanjeeva

AU - Toosi, Adel N.

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AB - As the grid is loosely coupled congregation of geographically distributed and heterogeneous resources, the efficient scheduling of their resources require the support of a sound Performance Prediction System (PPS). The performance prediction of resources is helpful for both Resource Management Systems and users to make optimized resource usage decisions. In this paper, it is discussed about the ways of several Machine Learning (ML) PPSs that span over several grid resources in several dimensions. Taxonomy is used to categorize and identify approaches which are followed in the implementation of the existing PPSs for grids and for their adaptation in the clouds. A test framework is proposed to evaluate their capability of serving in the clouds environment.

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KW - prediction

KW - Resources

KW - Taxonomy

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BT - 2019 Advances in Science and Engineering Technology International Conferences (ASET 2019)

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Seneviratne S, Witharana S, Toosi AN. Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds. In M. Khalid H, editor, 2019 Advances in Science and Engineering Technology International Conferences (ASET 2019). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. 8714535 https://doi.org/10.1109/ICASET.2019.8714535

Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds

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Keywords

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