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

摘要

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.).