Learning budget assignment policies for autoscaling scientific workflows in the cloud-Reference-Cited by-同舟云学术

Learning budget assignment policies for autoscaling scientific workflows in the cloud

Published:2019-02-09 Issue:1 Volume:23 Page:87-105
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Garí Yisel,Monge David A.^ORCID,Mateos Cristian,García Garino Carlos

Funder

Agencia Nacional de Promoci?n Cient?fica y Tecnol?gica

Secretar?a de Ciencia, T?cnica y Postgrado, Universidad Nacional de Cuyo

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/article/10.1007/s10586-018-02902-0/fulltext.html

Reference30 articles.

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2. Mao, M., Humphrey, M.: Scaling and scheduling to maximize application performance within budget constraints in cloud workflows. In: IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), 2013, pp. 67–78. IEEE (2013)

3. Monge, D.A., Garí, Y., Mateos, C., Garino, C.G.: Autoscaling scientific workflows on the cloud by combining on-demand and spot instances. Int. J. Comput. Syst. Sci. Eng. 32(4), 291–306 (2017). (Special Issue on Elastic Data Management in Cloud Systems)

4. Expósito, R.R., Taboada, G.L., Ramos, S., Touriño, J., Doallo, R.: Performance analysis of HPC applications in the cloud. Future Gen. Comput. Syst. 29(1), 218–229 (2013)

5. Ben-Yehuda, O.A., Ben-Yehuda, M., Schuster, A., Tsafrir, D.: Deconstructing amazon EC2 spot instance pricing. ACM Trans. Econ. Comput. 1(3), 16:1–16:20 (2013)

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