Experimental Analysis on Autonomic Strategies for Cloud Elasticity

International audienceIn 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

Experimental Analysis on Autonomic Strategies for Cloud Elasticity

International audienceIn 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

MemOpLight: Leveraging application feedback to improve container memory consolidation

International audienceThe container mechanism amortizes costs by consolidating several servers onto the same machine, while keeping them mutually isolated.Specifically, to ensure performance isolation, Linux relies on memory limits.These limits are static, despite the fact that application needs are dynamic; this results in poor performance.To solve this issue, MemOpLight uses dynamic application feedback to rebalance physical memory allocation between containers focusing on under-performing ones.This paper presents the issues, explains the design of MemOpLight, and validates it experimentally.Our approach increases total satisfaction by 13% compared to the default

Gestion autonomique de l'élasticité multi-couche des applications dans le Cloud : vers une utilisation efficiente des ressources et des services du Cloud

Cloud computing, through its layered model and access to its on-demand services, has changed the way of managing the infrastructures (IaaS) and how to produce software (SaaS). With the advent of IaaS elasticity, the amount of resources can be automatically adjusted according to the demand to satisfy a certain level of quality of service (QoS) to customers while minimizing underlying operating costs. The current elasticity model is based on adjusting the IaaS resources through basic autoscaling services, which reaches to its limit in terms of responsiveness and adaptation granularity. Although it is an essential feature for Cloud computing, elasticity remains poorly equipped which prevents the various actors of the Cloud to really enjoy its benefits. In this thesis, we propose to extend the concept of elasticity to higher layers of the cloud, and more precisely to the SaaS level. Then, we present the new concept of software elasticity by defining the ability of the software to adapt, ideally in an autonomous way, to cope with workload changes and/or limitations of IaaS elasticity. This predicament brings the consideration of Cloud elasticity in a multi-layer way through the adaptation of all kind of Cloud resources. To this end, we present a model for the autonomic management of multi-layer elasticity and the associated framework ElaStuff. In order to equip and industrialize the elasticity management process, we propose the perCEPtion monitoring tool, based on complex event processing, which enables the administrators to set up an advanced observation of the Cloud system. In addition, we propose a domain specific language (DSL) for the multi-layer elasticity, called ElaScript, which allows to simply and effectively express reconfiguration plans orchestrating the different levels of elasticity actions. Finally, our proposal to extend the Cloud elasticity to higher layers, particularly to SaaS,is validated experimentally from several perspectives (QoS,energy, responsiveness and accuracy of the scaling, etc.).L’informatique en nuage, au travers de son modèle en couche et de l’accès à ses services à la demande, a bouleversé la façon de gérer les infrastructures (IaaS) et la manière de produire les logiciels (SaaS). Grâce à l’élasticité de l’infrastructure, la quantité de ressource peut être ajustée automatiquement en fonction de la demande afin de satisfaire un certain niveau de qualité de service (QoS) aux clients tout en minimisant les coûts d’exploitation sous-jacents. Le modèle d’élasticité actuel qui consiste à ajuster les ressources IaaS au travers de services de dimensionnement automatique basiques montre ses limites en termes de réactivité et de granularité d’adaptation. De plus, bien qu’étant une caractéristique cruciale de l’informatique en nuage, l’élasticité est à ce jour pauvrement outillée empêchant ainsi les différents acteurs du Cloud de jouir pleinement de ses bienfaits. Dans ce travail de thèse, nous proposons d’étendre leconcept d’élasticité aux couches hautes du nuage, et plus précisément au niveau du SaaS. Nous présentons ainsi le nouveau concept d’élasticité logicielle que nous définissons comme la capacité d’un logiciel à s’adapter, idéalement de manière autonome, pour répondre aux changements de la demande et/ou aux limitations de l’élasticité des ressources de l’infrastructure. Il s’agit alors d’envisager l’élasticité de manière transverse et multi-couche en considérant l’adaptation des ressources Cloud au sens large. Pour ce faire, nous présentons un modèle pour la gestion autonome de l’élasticité multi-couche et le Framework ElaStuff associé. Dans le but d’outiller et d’industrialiser le processus de gestion de l’élasticité, nous proposons l’outil de surveillance perCEPtion basé sur le traitement des événements complexes et permettant à l’administrateur de mettre en place une observation avancée du système Cloud. De plus, un langage dédié à l’élasticité multi-couche nommé ElaScript est proposé pour exprimer simplement et efficacement des plans de reconfiguration orchestrant les actions d’élasticité de différents niveaux. Enfin, notre proposition d’étendre l’élasticité aux couches hautes du Cloud, et plus particulièrement au niveau SaaS, est validée expérimentalement selon plusieurs points devue (QoS, énergie, réactivité et précision du passage à l’échelle,etc.)