1. AQUAMan
- Author
-
Olivier Boissier, Yazan Mualla, Amro Najjar, Gauthier Picard, Laboratoire Hubert Curien [Saint Etienne] (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure des Mines de St Etienne, and Département Informatique et systèmes intelligents ( FAYOL-ENSMSE)
- Subjects
Service (business) ,Knowledge management ,business.industry ,Software as a service ,media_common.quotation_subject ,Control (management) ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Risk analysis (engineering) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Quality (business) ,Quality of experience ,business ,Adaptation (computer science) ,Average cost ,media_common - Abstract
International audience; As more interactive and multimedia-rich applications are migrating to the cloud, end-user satisfaction and her Quality of Experience (QoE) will become a determinant factor to secure success for any Software as a Service (SaaS) provider. Yet, in order to survive in this competitive market, SaaS providers also need to maximize their Quality of Business (QoBiz) and minimize costs paid to cloud providers. However, most of the existing works in the literature adopt a provider-centric approach where the end-user preferences are overlooked. In this article, we propose the AQUAMan mechanism that gives the provider a fine-grained QoE-driven control over the service acceptability rate while taking into account both end-users' satisfaction and provider's QoBiz. The proposed solution is implemented using a multi-agent simulation environment. The results show that the SaaS provider is capable of attaining the predefined acceptability rate while respecting the imposed average cost per user. Furthermore, the results help the SaaS provider identify the limits of the adaptation mechanism and estimate the best average cost to be invested per user.
- Published
- 2017