Home > Research > Publications & Outputs > Cloud Instance Selection Using Parallel K-Means...

Electronic data

Links

Text available via DOI:

View graph of relations

Cloud Instance Selection Using Parallel K-Means and AHP

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

Cloud Instance Selection Using Parallel K-Means and AHP. / Guo, Taiyang; Bahsoon, Rami; Chen, Tao et al.
Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM, 2019. p. 71-76.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Guo, T, Bahsoon, R, Chen, T, Elhabbash, A, Samreen, F & Elkhatib, Y 2019, Cloud Instance Selection Using Parallel K-Means and AHP. in Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM, pp. 71-76. https://doi.org/10.1145/3368235.3368845

APA

Guo, T., Bahsoon, R., Chen, T., Elhabbash, A., Samreen, F., & Elkhatib, Y. (2019). Cloud Instance Selection Using Parallel K-Means and AHP. In Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing (pp. 71-76). IEEE/ACM. https://doi.org/10.1145/3368235.3368845

Vancouver

Guo T, Bahsoon R, Chen T, Elhabbash A, Samreen F, Elkhatib Y. Cloud Instance Selection Using Parallel K-Means and AHP. In Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM. 2019. p. 71-76 doi: 10.1145/3368235.3368845

Author

Guo, Taiyang ; Bahsoon, Rami ; Chen, Tao et al. / Cloud Instance Selection Using Parallel K-Means and AHP. Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing. IEEE/ACM, 2019. pp. 71-76

Bibtex

@inproceedings{47019e32941f4c7fb9948b74272bf177,
title = "Cloud Instance Selection Using Parallel K-Means and AHP",
abstract = "Managing cloud spend and qualities when selecting cloud instances is cited as one of the timely research challenges in cloud computing. Cloud service consumers are often confronted by too many options and selection is challenging. This is because instance provision can be difficult to comprehend for an average technical user and tactics of cloud provider are far from being transparent biasing the selection. This paper proposes a novel cloud instance selection framework for finding the optimal IaaS purchase strategy for a VARD application in Amazon EC2. Analytical Hierarchy Process (AHP) and parallel K-Means Clustering algorithm are used and combined in Cloud Instance Selection environments. It allows cloud users to get the recommendation about cloud instance types and job submission periods based on requirements such as CPU, RAM, and resource utilisation. The system leverages AHP to select cloud instance type. Besides, AHP results are used by the parallel K-Means clustering model to find the best execution time for a given day according to the user's requirements. Finally, we provide an example to demonstrate the applicability of the approach. Experiments indicate that our approach achieves better results than ad-hoc and cost-driven approaches.",
author = "Taiyang Guo and Rami Bahsoon and Tao Chen and Abdessalam Elhabbash and Faiza Samreen and Yehia Elkhatib",
year = "2019",
month = dec,
day = "1",
doi = "10.1145/3368235.3368845",
language = "English",
isbn = "9781450370448",
pages = "71--76",
booktitle = "Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing",
publisher = "IEEE/ACM",

}

RIS

TY - GEN

T1 - Cloud Instance Selection Using Parallel K-Means and AHP

AU - Guo, Taiyang

AU - Bahsoon, Rami

AU - Chen, Tao

AU - Elhabbash, Abdessalam

AU - Samreen, Faiza

AU - Elkhatib, Yehia

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Managing cloud spend and qualities when selecting cloud instances is cited as one of the timely research challenges in cloud computing. Cloud service consumers are often confronted by too many options and selection is challenging. This is because instance provision can be difficult to comprehend for an average technical user and tactics of cloud provider are far from being transparent biasing the selection. This paper proposes a novel cloud instance selection framework for finding the optimal IaaS purchase strategy for a VARD application in Amazon EC2. Analytical Hierarchy Process (AHP) and parallel K-Means Clustering algorithm are used and combined in Cloud Instance Selection environments. It allows cloud users to get the recommendation about cloud instance types and job submission periods based on requirements such as CPU, RAM, and resource utilisation. The system leverages AHP to select cloud instance type. Besides, AHP results are used by the parallel K-Means clustering model to find the best execution time for a given day according to the user's requirements. Finally, we provide an example to demonstrate the applicability of the approach. Experiments indicate that our approach achieves better results than ad-hoc and cost-driven approaches.

AB - Managing cloud spend and qualities when selecting cloud instances is cited as one of the timely research challenges in cloud computing. Cloud service consumers are often confronted by too many options and selection is challenging. This is because instance provision can be difficult to comprehend for an average technical user and tactics of cloud provider are far from being transparent biasing the selection. This paper proposes a novel cloud instance selection framework for finding the optimal IaaS purchase strategy for a VARD application in Amazon EC2. Analytical Hierarchy Process (AHP) and parallel K-Means Clustering algorithm are used and combined in Cloud Instance Selection environments. It allows cloud users to get the recommendation about cloud instance types and job submission periods based on requirements such as CPU, RAM, and resource utilisation. The system leverages AHP to select cloud instance type. Besides, AHP results are used by the parallel K-Means clustering model to find the best execution time for a given day according to the user's requirements. Finally, we provide an example to demonstrate the applicability of the approach. Experiments indicate that our approach achieves better results than ad-hoc and cost-driven approaches.

U2 - 10.1145/3368235.3368845

DO - 10.1145/3368235.3368845

M3 - Conference contribution/Paper

SN - 9781450370448

SP - 71

EP - 76

BT - Proceedings of IEEE/ACM 12th International Conference on Utility and Cloud Computing

PB - IEEE/ACM

ER -