Reducing the price of resource provisioning using EC2 spot instances with prediction models

Fabra, J. (Universidad de Zaragoza) ; Ezpeleta, J. (Universidad de Zaragoza) ; Álvarez, P. (Universidad de Zaragoza)

Resumen: The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed.

Idioma: Inglés

DOI: 10.1016/j.future.2019.01.025

Año: 2019

Publicado en: Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications 96 (2019), 348-367
ISSN: 0167-739X

Factor impacto JCR: 6.125 (2019)

Categ. JCR: COMPUTER SCIENCE, THEORY & METHODS rank: 8 / 108 = 0.074 (2019) - Q1 - T1

Factor impacto SCIMAGO: 1.216 - Computer Networks and Communications (Q1) - Software (Q1) - Hardware and Architecture (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T21-17R-DISCO
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R
Financiación: info:eu-repo/grantAgreement/ES/UZ/JIUZ-2018-TEC-04

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)