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

Sena Seneviratne, Sanjeeva Witharana, Adel N. Toosi

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

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 languageEnglish
Title of host publication2019 Advances in Science and Engineering Technology International Conferences (ASET 2019)
EditorsHaris M. Khalid
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538682715
ISBN (Print)9781538682722
DOIs
Publication statusPublished - 2019
Event2019 Advances in Science and Engineering Technology International Conferences - Dubai, United Arab Emirates
Duration: 26 Mar 201928 Mar 2019
http://www.aset.hct.ac.ae/aset2019/

Conference

Conference2019 Advances in Science and Engineering Technology International Conferences
Abbreviated titleASET 2019
CountryUnited Arab Emirates
CityDubai
Period26/03/1928/03/19
Internet address

Keywords

  • clouds
  • grids
  • prediction
  • Resources
  • Taxonomy

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] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASET.2019.8714535
Seneviratne, Sena ; Witharana, Sanjeeva ; Toosi, Adel N. / Adapting the Machine Learning grid prediction models for forecasting of resources on the clouds. 2019 Advances in Science and Engineering Technology International Conferences (ASET 2019). editor / Haris M. Khalid. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 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 proceedingConference PaperOther

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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