Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in Cloud platforms

Kousiouris, G; Menychtas, A; Kyriazis, D; Gogouvitis, S; Varvarigou, T

dc.contributor.author	Kousiouris, G	en
dc.contributor.author	Menychtas, A	en
dc.contributor.author	Kyriazis, D	en
dc.contributor.author	Gogouvitis, S	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T11:46:47Z
dc.date.available	2014-03-01T11:46:47Z
dc.date.issued	2012	en
dc.identifier.issn	0167739X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/38030
dc.subject	Artificial neural networks	en
dc.subject	Cloud computing	en
dc.subject	Performance estimation	en
dc.subject	SLA translation	en
dc.subject	Workload prediction	en
dc.title	Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in Cloud platforms	en
heal.type	other	en
heal.identifier.primary	10.1016/j.future.2012.05.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.future.2012.05.009	en
heal.publicationDate	2012	en
heal.abstract	Delivering Internet-scale services and IT-enabled capabilities as computing utilities has been made feasible through the emergence of Cloud environments. While current approaches address a number of challenges such as quality of service, live migration and fault tolerance, which is of increasing importance, refers to the embedding of users' and applications' behaviour in the management processes of Clouds. The latter will allow for accurate estimation of the resource provision (for certain levels of service quality) with respect to the anticipated users' and applications' requirements. In this paper we present a two-level generic black-box approach for behavioral-based management across the Cloud layers (i.e., Software, Platform, Infrastructure): it provides estimates for resource attributes at a low level by analyzing information at a high level related to application terms (Translation level) while it predicts the anticipated user behaviour (Behavioral level). Patterns in high-level information are identified through a time series analysis, and are afterwards translated to low-level resource attributes with the use of Artificial Neural Networks. We demonstrate the added value and effectiveness of the Translation level through different application scenarios: namely FFMPEG encoding, real-time interactive e-Learning and a Wikipedia-type server. For the latter, we also validate the combined level model through a trace-driven simulation for identifying the overall error of the two-level approach. © 2012 Elsevier B.V. All rights reserved.	en
heal.journalName	Future Generation Computer Systems	en
dc.identifier.doi	10.1016/j.future.2012.05.009	en