Experimental Analysis on Autonomic Strategies for Cloud Elasticity - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Experimental Analysis on Autonomic Strategies for Cloud Elasticity

Abstract

In spite of the indubitable advantages of elasticity in Cloud infrastructures, some technical and conceptual limitations are still to be considered. For instance , resource start up time is generally too long to react to unexpected workload spikes. Also, the billing cycles' granularity of existing pricing models may incur consumers to suffer from partial usage waste. We advocate that the software layer can take part in the elasticity process as the overhead of software reconfigurations can be usually considered negligible if compared to infrastructure one. Thanks to this extra level of elasticity, we are able to define cloud reconfigurations that enact elasticity in both software and infrastructure layers so as to meet demand changes while tackling those limitations. This paper presents an autonomic approach to manage cloud elasticity in a cross-layered manner. First, we enhance cloud elasticity with the software elasticity model. Then, we describe how our au-tonomic cloud elasticity model relies on dynamic selection of elasticity tactics. We present an experimental analysis of a subset of those elasticity tactics under different scenarios in order to provide insights on strategies that could drive the autonomic selection of the proper tactics to be applied.
Fichier principal
Vignette du fichier
CAC2015-sdupont.pdf (622.81 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01178419 , version 1 (20-07-2015)

Licence

Attribution

Identifiers

Cite

Simon Dupont, Jonathan Lejeune, Frederico Alvares, Thomas Ledoux. Experimental Analysis on Autonomic Strategies for Cloud Elasticity. 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC), Sep 2015, Cambridge, United States. ⟨10.1109/ICCAC.2015.22⟩. ⟨hal-01178419⟩
612 View
386 Download

Altmetric

Share

Gmail Facebook X LinkedIn More