. Élasticité-multi-couche, exemple Dans le cadre de nos travaux portant sur l'élasticité dans le Cloud, nous avons entrepris, dès la première année de thèse, de valider nos intuitions quant au fait d'étendre les capacités d'adaptation du système en adressant les ressources informatiques des couches hautes du Nuage. Ainsi, un projet autour de l'élasticité logicielle (i.e. SaaS), faisant office de preuve de concept

.. Multi-couche, 85 4.2.1 Architecture du framework, SCUBA : vers une gestion de l'élasticité

. Enfin, nous portons une réflexion sur le passage à l'échelle de notre solution de gestion autonomique de l'élasticité multi-couche dans la section 8

. Qof-et-consommation-Énergétique, Nous allons nous intéresser ici à la QoF de l'application RUBiS ainsi qu'à la dimension énergétique de l'élasticité logicielle. La Figure 8.8 présente la QoF de l'application pour le scénario Wikipedia c'est-à-dire les Of f sof t utilisées pour les 4 expériences réalisées. On constate que les trois premières implémentations, à savoir noreco en bleu, onereco en orange et tworeco en vert, admettent la même Of f sof t tout le long de l'expérience (resp. 0, 1 et 2). L'implémentation QoSaware en rouge, quant à elle

L. Figure and 8. , QoSaware commence à déclencher des reconfigurations à partir de 45min, ce qui correspond au moment où la charge entrante du scénario Wikipedia devient importante, et ce jusqu'à la fin du scénario. On remarquera aussi que l'Of f sof t devient constante à 0 pendant environ 10min, Ces 10min correspondent pourtant au moment où l'on essuie des problèmes de QoS (cf. Figures 8.6d et 8.7d) alors qu'on pourrait s'attendre à une bonne QoS du fait que la QoF est à son minimum (i.e. compromis QoF-QoS)

. Enfin, QoSaware donne ici l'impression d'osciller fortement. Cela peut s'expliquer en partie du fait que nous avons fixé une prise de décision toutes les 20sec, En effet, nous avons ramené le scénario Wikipedia originel d'une durée de 24h à 96min pour nos expériences

. Ainsi, les 20sec correspondent en réalité à 5min, ce qui est davantage raisonnable. Cependant, on pourrait aussi imaginer ajouter une période de calme suite à une reconfiguration (i.e. une instruction

. Un-modèle-conceptuel-de-l-'élasticité-multi-couche, nous avons défini et modélisé l'élasticité logicielle ainsi que les différents concepts relatifs à cette nouvelle capacité d'adaptation. Nous avons introduit un modèle de ressources Cloud qui consiste en une représentation du système sous forme de graphe de ressources. Enfin, nous avons proposé une modélisation de l'élasticité multi-couche qui revient à

. Un-modèle-de-surveillance-outillé, nous avons proposé un modèle conceptuel de surveillance reposant sur le traitement des événements complexes dans le but de définir une observation avancée des ressources Cloud au sens large Une implémentation de ce modèle a été proposée au travers de l'outil perCEPtion qui permet à l'administrateur Cloud de manipuler le modèle de surveillance en définissant en amont les symptômes, signes d'incohérence, qui seront identifiés à l'exécution et pourront faire l

. Étendre-l-'adaptation and . Cloud, dans cette thèse, nous proposons d'étendre l'élasticité aux couches hautes du Cloud, et plus précisément à la couche SaaS, en considérant les ressources qui la constituent à savoir les composants logiciels. Cela se traduit par de nouvelles possibilités de reconfiguration faisant intervenir de nouvelles dimensions d'adaptation, Cependant, nous ne prétendons pas être exhaustif dans les adaptations possibles offertes par un environnement Cloud

E. Effet, autres types de ressources comme les conteneurs logiciels (e.g. Docker [DOC15]), que l'on peut associer à la couche PaaS, et qui admettent des propriétés intéressantes qui permettraient notamment d'accroître la réactivité du passage à l'échelle (par rapport à une VM) De même, il serait possible d'intégrer de nouvelles actions d'adaptation comme la migration de VM ou l

. Dans-cette-voie, on pourrait s'appuyer sur les travaux autour de la modélisation à l'exécution (i.e. model@runtime [BBF09] [Fou13]) pour constituer une représentation simplifiée d'un système Cloud réel (i.e. abstraction), qui en contient les aspects fondamentaux (i.e. architecture, contexte d'exécution, etc

. Modéliser-et-gérer-la-variabilité and . Cloud, Dans ce domaine, les variations du système sont le résultat d'exigences fonctionnelles distinctes des utilisateurs Quelques travaux se sont intéressés à la modélisation et la gestion de la variabilité dans un contexte Cloud en partant du principe que la variabilité d'un tel système, induite par les différentes configurations possibles, nécessite d'être modélisée afin de faciliter sa manipulation et son partage entre les différents acteurs du Cloud. Certains travaux se sont appliqués à intégrer la dimension non-fonctionnelle (i.e. performances, QoS, etc.) aux modèles de variabilités existants comme c'est le cas de [SMM + 12] qui s'intéresse à une configuration transverse des différentes couches du Cloud. De même, [GGRTRC13] s'appuie sur les feature model [KCH + 90] dans le but de modéliser la variabilité IaaS des différentes offres de machines virtuelles de Amazon Web Service [AWS16]. Enfin, [HHPS08] s'intéresse à la notion de variabilité dans le contexte dynamique d'applications SaaS déployées sur le Cloud. Le besoin de modélisation de la variabilité prend tout son sens dans le contexte de notre travail autour de l'élasticité multi-couche pour laquelle les différents types de ressources concernés peuvent faire l'objet de configurations diverses et variées (e.g. Of f inf ra , Of f sof t ) admettant des résultats distincts en termes de coût, etc. Dans le cas de l'élasticité logicielle, il serait intéressant de s'appuyer sur une telle modélisation de la variabilité dans le but de générer automatiquement et dynamiquement les différentes variantes de l'application SaaS (i.e. Of f sof t

. Dans-cette-voie, propose une solution basée sur l'ingénierie dirigée par les modèles [Sch06] permettant de gérer la variabilité en générant automatiquement différentes variantes de l'application SaaS. Cependant, la variabilité n'est pas résolue de manière dynamique et consiste à déployer plusieurs instances de l'application en amont, Nous proposons donc de poursuivre les efforts amorcés dans cette voie en considérant une résolution dynamique de la variabilité

[. Bibliographie, M. A. Ahmad, and . Babar, Towards a pattern language for self-adaptation of cloud-based architectures, Proceedings of the WICSA 2014 Companion, p.72, 2014.

V. Andrikopoulos, T. Binz, F. Leymann, and S. Strauch, How to adapt applications for the Cloud environment, Computing, vol.42, issue.4, pp.493-535, 2013.
DOI : 10.1007/s00607-012-0248-2

M. Alhamad, T. Dillon, and E. Chang, Conceptual SLA framework for cloud computing, 4th IEEE International Conference on Digital Ecosystems and Technologies, pp.606-610, 2010.
DOI : 10.1109/DEST.2010.5610586

A. Ali-eldin, J. Tordsson, and E. Elmroth, An adaptive hybrid elasticity controller for cloud infrastructures, 2012 IEEE Network Operations and Management Symposium, pp.204-212, 2012.
DOI : 10.1109/NOMS.2012.6211900

A. Armbrust, R. Fox, . Griffith, D. Anthony, R. Joseph et al., A view of cloud computing, Communications of the ACM, vol.53, issue.4, pp.50-58, 2010.
DOI : 10.1145/1721654.1721672

C. Appuswamy, D. Gkantsidis, O. Narayanan, A. Hodson, and . Rowstron, Scale-up vs scale-out for Hadoop, Proceedings of the 4th annual Symposium on Cloud Computing, SOCC '13, p.20, 2013.
DOI : 10.1145/2523616.2523629

M. Arlitt and T. Jin, 1998 world cup web site access logs, p.49, 1998.

H. Alipour, Y. Liu, and A. Hamou-lhadj, Analyzing auto-scaling issues in cloud environments, Proceedings of 24th Annual International Conference on Computer Science and Software Engineering, pp.75-89, 2014.

. Antlr, Another tool for language recognition, pp.2015-128

A. Beloglazov, J. Abawajy, and R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, pp.755-768, 2012.

O. Barais, Utilisation de la modélisation à l'exécution : objectif, challenges et bénéfices, p.107, 2014.

F. David, C. Bantz, D. Bisdikian, . Challener, P. John et al., Autonomic personal computing, IBM Systems Journal, vol.42, issue.1, pp.165-176, 2003.

G. Blair, N. Bencomo, and R. France, Models@ run.time, Computer, vol.42, issue.10, pp.22-27, 2009.
DOI : 10.1109/MC.2009.326

URL : https://hal.archives-ouvertes.fr/hal-00733338

E. Bruneton, T. Coupaye, M. Leclercq, V. Quéma, and J. Stefani, The fractal component model and its support in java. Software : Practice and Experience, pp.11-121257, 2006.

L. Bettini, Implementing Domain-Specific Languages with Xtext and Xtend, p.129, 2013.

E. Barrett, E. Howley, and J. Duggan, Applying reinforcement learning towards automating resource allocation and application scalability in the cloud Concurrency and Computation : Practice and Experience, pp.1656-1674, 2013.

D. Bouyssou, Building Criteria: A Prerequisite for MCDA, Readings in multiple criteria decision aid, pp.58-80, 1990.
DOI : 10.1007/978-3-642-75935-2_4

C. Paul and . Brebner, Is your cloud elastic enough ? : performance modelling the elasticity of infrastructure as a service (iaas) cloud applications, Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering, pp.263-266, 2012.

X. Bu, J. Rao, and C. Xu, Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. Parallel and Distributed Systems, IEEE Transactions on, vol.24, issue.55, pp.681-690, 2013.

R. Buyya, C. Shin-yeo, and S. Venugopal, Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities, 2008 10th IEEE International Conference on High Performance Computing and Communications, pp.5-13, 2008.
DOI : 10.1109/HPCC.2008.172

C. Buyya, S. Shin-yeo, J. Venugopal, I. Broberg, and . Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, vol.25, issue.6, pp.599-616, 2009.
DOI : 10.1016/j.future.2008.12.001

C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul et al., Live migration of virtual machines, Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation, pp.273-286, 2005.

D. Clements, L. Garlan, J. Bass, and . Stafford, Documenting software architectures: views and beyond, 25th International Conference on Software Engineering, 2003. Proceedings., p.35, 2002.
DOI : 10.1109/ICSE.2003.1201264

D. Shang-wen-cheng, B. Garlan, and . Schmerl, Architecture-based self-adaptation in the presence of multiple objectives, Proceedings of the 2006 international workshop on Self-adaptation and self-managing systems, pp.2-8

D. Shang-wen-cheng, B. Garlan, and . Schmerl, Evaluating the effectiveness of the rainbow self-adaptive system, Software Engineering for Adaptive and Self-Managing Systems, 2009. SEAMS'09. ICSE Workshop on, pp.132-141, 2009.

W. Chen, J. He, S. Liu, L. Nath, L. Rigas et al., Energy-aware server provisioning and load dispatching for connection-intensive internet services, NSDI, pp.337-350, 2008.

G. Cugola and A. Margara, Processing flows of information, ACM Computing Surveys, vol.44, issue.3, pp.15-113, 2012.
DOI : 10.1145/2187671.2187677

[. Copil, D. Moldovan, H. Truong, and S. Dustdar, Multi-level Elasticity Control of Cloud Services, Service-Oriented Computing, pp.429-436, 2013.
DOI : 10.1007/978-3-642-45005-1_31

[. Copil, D. Moldovan, H. Truong, and S. Dustdar, SYBL: An Extensible Language for Controlling Elasticity in Cloud Applications, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp.112-119, 2013.
DOI : 10.1109/CCGrid.2013.42

E. Casalicchio and L. Silvestri, Autonomic Management of Cloud-Based Systems: The Service Provider Perspective, Computer and Information Sciences III, pp.39-47, 2013.
DOI : 10.1007/978-1-4471-4594-3_5

E. Daubert, Adaptation et cloud computing : un besoin d'abstraction pour une gestion transverse, p.71, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00904364

S. Dutta, S. Gera, A. Verma, and B. Viswanathan, SmartScale: Automatic Application Scaling in Enterprise Clouds, 2012 IEEE Fifth International Conference on Cloud Computing, pp.221-228
DOI : 10.1109/CLOUD.2012.12

G. Thomas and . Dietterich, Machine learning for sequential data : A review, Structural, syntactic, and statistical pattern recognition, pp.15-30, 2002.

S. Xavier-dutreilh, O. Kirgizov, J. Melekhova, and . Malenfant, Nicolas Rivierre, and Isis Truck Using reinforcement learning for autonomic resource allocation in clouds : Towards a fully automated workflow, ICAS 2011, The Seventh International Conference on Autonomic and Autonomous Systems, pp.67-74, 2011.

J. Domaschka, K. Kritikos, and A. Rossini, Towards a Generic Language for Scalability Rules, Advances in Service-Oriented and Cloud Computing, pp.206-220, 2014.
DOI : 10.1007/978-3-319-14886-1_19

S. Dupont, J. Lejeune, F. Alvares, and T. Ledoux, Experimental Analysis on Autonomic Strategies for Cloud Elasticity, 2015 International Conference on Cloud and Autonomic Computing, 2015.
DOI : 10.1109/ICCAC.2015.22

URL : https://hal.archives-ouvertes.fr/hal-01178419

. Docker, Document object model (dom). https, pp.205-144, 2015.

W. Dawoud, I. Takouna, and C. Meinel, Elastic VM for Cloud Resources Provisioning Optimization, Advances in Computing and Communications, pp.431-445, 2011.
DOI : 10.1145/1496909.1496922

M. Eysholdt and H. Behrens, Xtext, Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion, SPLASH '10, pp.307-309, 2010.
DOI : 10.1145/1869542.1869625

M. Ehrgott, Multicriteria optimization, p.105, 2013.
DOI : 10.1007/978-3-662-22199-0

J. Oriol-fitó, I. Goiri, and J. Guitart, Sla-driven elastic cloud hosting provider, Parallel, Distributed and Network-Based Processing (PDP), 2010 18th Euromicro International Conference on, pp.111-118, 2010.

M. Fiedler, T. Hossfeld, and P. Tran-gia, A generic quantitative relationship between quality of experience and quality of service, IEEE Network, vol.24, issue.2, pp.36-41, 2010.
DOI : 10.1109/MNET.2010.5430142

W. Fang, Z. Lu, J. Wu, and Z. Cao, RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center, 2012 IEEE Ninth International Conference on Services Computing, pp.609-616
DOI : 10.1109/SCC.2012.47

F. Fouquet, Kevoree : Model@ Runtime pour le développement continu de systèmes adaptatifs distribués hétérogènes, p.205, 2013.

M. Fowler, Domain-specific languages. Pearson Education [g5k15] Grid5000. https://www.grid5000.fr/mediawiki/index.php/ Grid5000:Home, 2015, pp.122-190, 2010.

L. M. Garshol, Bnf and ebnf : What are they and how do they work. acedida pela última vez em, pp.126-2015, 2003.

G. Galante, L. Carlos, and E. Bona, A Survey on Cloud Computing Elasticity, 2012 IEEE Fifth International Conference on Utility and Cloud Computing, pp.263-270, 2012.
DOI : 10.1109/UCC.2012.30

D. Garlan, . Shang-wen, A. Cheng, B. Huang, P. Schmerl et al., Rainbow: architecture-based self-adaptation with reusable infrastructure, Computer, vol.37, issue.10, pp.46-54, 2004.
DOI : 10.1109/MC.2004.175

J. Garc?a-galán, P. Of-rana, A. Trinidad, and . Ruiz-cortés, Migrating to the cloud : a software product line based analysis, 3rd International Conference on Cloud Computing and Services Science (CLOSER), pp.416-426, 2013.

Z. Gong, X. Gu, and J. Wilkes, Press : Predictive elastic resource scaling for cloud systems, Network and Service Management (CNSM), 2010 International Conference on, pp.9-16, 2010.

E. Gamma, R. Helm, R. Johnson, and J. Vlissides, Design patterns : elements of reusable object-oriented software. Pearson Education, 1994. 118, 121 [goo15a] Google map. https://www.google.fr/maps, Goo15b] Google Compute Engine Autoscaler, pp.78-129, 2015.

D. Garlan, B. Schmerl, and S. Cheng, Software Architecture-Based Self-Adaptation, Autonomic computing and networking, pp.31-55, 2009.
DOI : 10.1007/978-0-387-89828-5_2

B. Gsl-+-12-]-hamoun-ghanbari, M. Simmons, C. Litoiu, G. Barna, and . Iszlai, Optimal autoscaling in a iaas cloud, Proceedings of the 9th international conference on Autonomic computing, pp.173-178, 2012.

H. Ghanbari, B. Simmons, M. Litoiu, and G. Iszlai, Exploring Alternative Approaches to Implement an Elasticity Policy, 2011 IEEE 4th International Conference on Cloud Computing, pp.716-723, 2011.
DOI : 10.1109/CLOUD.2011.101

. Han, M. Moustafa, L. Ghanem, Y. Guo, M. Guo et al., Enabling cost-aware and adaptive elasticity of multi-tier cloud applications, Future Generation Computer Systems, vol.32, issue.46, pp.82-98, 2014.
DOI : 10.1016/j.future.2012.05.018

R. Han, L. Guo, M. Moustafa, Y. Ghanem, and . Guo, Lightweight Resource Scaling for Cloud Applications, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pp.644-651
DOI : 10.1109/CCGrid.2012.52

S. Hallsteinsen, M. Hinchey, S. Park, and K. Schmid, Dynamic Software Product Lines, Computer, vol.41, issue.4, pp.93-95, 2008.
DOI : 10.1109/MC.2008.123

URL : https://hal.archives-ouvertes.fr/hal-00722377

S. Nikolas-roman-herbst, R. Kounev, and . Reussner, Elasticity in cloud computing : What it is, and what it is not, ICAC, pp.23-27, 2013.

C. Markus, J. A. Huebscher, and . Mccann, A survey of autonomic computing?degrees, models, and applications, ACM Computing Surveys (CSUR), vol.40, issue.3, pp.7-26, 2008.

S. Haykin and N. Network, A comprehensive foundation, Neural Networks, vol.2, p.41, 2004.

P. Horn, Autonomic computing : Ibm\'s perspective on the state of information technology, p.104, 2001.

M. Httermann, DevOps for developers. Apress, p.123, 2012.

J. Heo, X. Zhu, P. Padala, and Z. Wang, Memory overbooking and dynamic control of xen virtual machines in consolidated environments, Integrated Network Management, 2009. IM'09. IFIP/IEEE International Symposium on, pp.630-637, 2009.

W. Iqbal, N. Matthew, D. Dailey, P. Carrera, and . Janecek, Adaptive resource provisioning for read intensive multi-tier applications in the cloud, Future Generation Computer Systems, vol.27, issue.6, pp.871-879, 2011.
DOI : 10.1016/j.future.2010.10.016

S. Islam, J. Keung, K. Lee, and A. Liu, Empirical prediction models for adaptive resource provisioning in the cloud, Future Generation Computer Systems, vol.28, issue.1, pp.155-162, 2012.
DOI : 10.1016/j.future.2011.05.027

P. Jamshidi, A. Ahmad, and C. Pahl, Autonomic resource provisioning for cloud-based software, Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2014, pp.95-104
DOI : 10.1145/2593929.2593940

M. Jung, . Hiltunen, R. Kaustubh, . Joshi, D. Richard et al., Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures, 2010 IEEE 30th International Conference on Distributed Computing Systems, pp.62-73, 2010.
DOI : 10.1109/ICDCS.2010.88

Z. Jaroucheh, X. Liu, and S. Smith, A model-driven approach to flexible multi-level customization of saas applications, Proc. 22nd Int. Conf. Software Engineering and Knowledge Engineering (SEKE ¡ 10), pp.241-246, 2010.

J. Jiang, J. Lu, G. Zhang, and G. Long, Optimal Cloud Resource Auto-Scaling for Web Applications, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp.58-65, 2013.
DOI : 10.1109/CCGrid.2013.73

V. Li-jie-jin, A. Machiraju, and . Sahai, Analysis on service level agreement of web services, pp.19-34, 2002.

F. Junior, Multi Autonomic Management for Optimizing Energy Consumption in Cloud Infrastructures, p.63, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00853575

H. Jin, X. Wang, S. Wu, S. Di, and X. Shi, Towards Optimized Fine-Grained Pricing of IaaS Cloud Platform, IEEE Transactions on Cloud Computing, vol.3, issue.4, p.32, 2014.
DOI : 10.1109/TCC.2014.2344680

P. Kranas, V. Anagnostopoulos, A. Menychtas, and T. Varvarigou, ElaaS: An Innovative Elasticity as a Service Framework for Dynamic Management across the Cloud Stack Layers, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems, pp.1042-1049
DOI : 10.1109/CISIS.2012.117

O. Jeffrey, . Kephart, M. David, and . Chess, The vision of autonomic computing, Computer, vol.36, issue.106, pp.41-50, 2003.

C. Kyo, . Kang, G. Sholom, . Cohen, A. James et al., Feature-oriented domain analysis (foda) feasibility study, p.205, 1990.

S. Kächele, J. Domaschka, J. Franz, and . Hauck, COSCA, Proceedings of the First International Workshop on Cloud Computing Platforms, CloudCP '11, pp.4-53, 2011.
DOI : 10.1145/1967422.1967426

Y. Kouki, F. Alvares-de-oliveira, S. Dupont, and T. Ledoux, A Language Support for Cloud Elasticity Management, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp.206-215
DOI : 10.1109/CCGrid.2014.17

URL : https://hal.archives-ouvertes.fr/hal-00941945

K. Kritikos, J. Domaschka, and A. Rossini, SRL: A Scalability Rule Language for Multi-cloud Environments, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, pp.1-9, 2014.
DOI : 10.1109/CloudCom.2014.170

T. Knauth and C. Fetzer, Scaling Non-elastic Applications Using Virtual Machines, 2011 IEEE 4th International Conference on Cloud Computing, pp.468-475, 2011.
DOI : 10.1109/CLOUD.2011.77

Y. Kouki and T. Ledoux, SLA-driven capacity planning for Cloud applications, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp.135-140, 2012.
DOI : 10.1109/CloudCom.2012.6427519

URL : https://hal.archives-ouvertes.fr/hal-00734417

[. Klein, M. Maggio, K. Årzén, and F. Hernández-rodriguez, Brownout: building more robust cloud applications, Proceedings of the 36th International Conference on Software Engineering, ICSE 2014, pp.700-711
DOI : 10.1145/2568225.2568227

N. Nilesh, J. M. Karnik, Q. Mendel, and . Liang, Type-2 fuzzy logic systems, Fuzzy Systems IEEE Transactions on, vol.7, issue.6, pp.643-658, 1999.

J. Koomey, Growth in data center electricity use A report by Analytical Press, completed at the request of The New York Times, pp.9-16, 2005.

Y. Kouki, Approche dirigée par les contrats de niveaux de service pour la gestion de l'élasticité du" nuage, pp.65-88, 2013.

K. Kritikos and D. Plexousakis, Semantic QoS Metric Matching, 2006 European Conference on Web Services (ECOWS'06), pp.265-274, 2006.
DOI : 10.1109/ECOWS.2006.34

G. Klir and B. Yuan, Fuzzy sets and fuzzy logic, p.38, 1995.

A. Khan, X. Yan, S. Tao, and N. Anerousis, Workload characterization and prediction in the cloud: A multiple time series approach, 2012 IEEE Network Operations and Management Symposium, pp.1287-1294, 2012.
DOI : 10.1109/NOMS.2012.6212065

C. Harold, S. Lim, . Babu, S. Jeffrey, . Chase et al., Automated control in cloud computing : challenges and opportunities, Proceedings of the 1st workshop on Automated control for datacenters and clouds, pp.13-18, 2009.

T. Lorido-botrán, J. Miguel-alonso, and J. A. Lozano, A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments, Journal of Grid Computing, vol.5, issue.4, pp.2012-80, 2012.
DOI : 10.1007/s10723-014-9314-7

T. Lorido-botran, J. Miguel-alonso, and J. A. Lozano, A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments, Journal of Grid Computing, vol.5, issue.4, pp.559-592, 2014.
DOI : 10.1007/s10723-014-9314-7

L. Letondeur, Planification pour la gestion autonomique de l'élasticité d'applications dans le cloud, p.66, 2014.

K. Bean, L. R. Moore, and T. Ellahi, A coordinated reactive and predictive approach to cloud elasticity, The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization, 2013. iv, pp.47-72

L. Liu, Y. Sha, S. Diao, . Froehlich, L. Joseph et al., Online Response Time Optimization of Apache Web Server, Quality of Service?IWQoS 2003, pp.461-478, 2003.
DOI : 10.1007/3-540-44884-5_25

J. Liu, J. Tong, R. Mao, J. Bohn, L. Messina et al., Nist cloud computing reference architecture, NIST special publication, vol.500, issue.9, p.292, 2011.
DOI : 10.6028/NIST.SP.500-292

R. Marler, S. Jasbir, and . Arora, Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, pp.369-395, 2004.

A. Daniel, . Menasce, A. Virgilio, . Almeida, W. Lawrence et al., Performance by design : computer capacity planning by example, p.23, 2004.

P. Maes, Concepts and experiments in computational reflection, ACM SIGPLAN Notices, vol.22, issue.12, pp.147-155, 1987.
DOI : 10.1145/38807.38821

M. Maurer, I. Brandic, and R. Sakellariou, Enacting SLAs in Clouds Using Rules, Euro-Par 2011 Parallel Processing, pp.455-466, 2011.
DOI : 10.1016/j.comnet.2009.04.014

M. Maurer, I. Brandic, and R. Sakellariou, Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures, 2012 IEEE Fifth International Conference on Cloud Computing, pp.368-375
DOI : 10.1109/CLOUD.2012.55

D. Moldovan, G. Copil, H. Truong, S. Dustdar, and . Mela, MELA: Monitoring and Analyzing Elasticity of Cloud Services, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, pp.80-87, 2013.
DOI : 10.1109/CloudCom.2013.18

C. Meinel, W. Dawoud, and I. Takouna, Elastic vm for dynamic virtualized resources provisioning and optimization. HPI Future SOC Lab : proceedings, pp.13-72, 2011.

B. Meyer, Applying 'design by contract', Computer, vol.25, issue.10, pp.40-51, 1992.
DOI : 10.1109/2.161279

P. Mell and T. Grance, The nist definition of cloud computing, p.58, 2011.
DOI : 10.6028/NIST.SP.800-145

M. Mao and M. Humphrey, A Performance Study on the VM Startup Time in the Cloud, 2012 IEEE Fifth International Conference on Cloud Computing, pp.423-430, 2012.
DOI : 10.1109/CLOUD.2012.103

M. Mao and M. Humphrey, Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, pp.67-78
DOI : 10.1109/IPDPS.2013.61

M. Mernik, J. Heering, M. Anthony, and . Sloane, When and how to develop domain-specific languages, ACM Computing Surveys, vol.37, issue.4, pp.316-344, 2005.
DOI : 10.1145/1118890.1118892

T. Mastelic, A. Oleksiak, H. Claussen, I. Brandic, J. Pierson et al., Cloud Computing, ACM Computing Surveys, vol.47, issue.2, pp.33-49, 2014.
DOI : 10.1145/2656204

URL : https://hal.archives-ouvertes.fr/hal-01153714

J. Marino and M. Rowley, Understanding sca (service component architecture ). Pearson Education, p.65, 2009.

P. Merle, R. Rouvoy, L. Seinturier, and . Frascati, Adaptive and reflective middleware of middleware, 12th ACM/IFIP/USENIX International Middleware Conference, p.71, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650608

C. Cassales-marquezan, F. Wessling, A. Metzger, K. Pohl, C. Woods et al., Towards exploiting the full adaptation potential of cloud applications, Proceedings of the 6th International Workshop on Principles of Engineering Service-Oriented and Cloud Systems, pp.48-57, 0190.

V. Nikolov, S. Kachele, J. Franz, D. Hauck, and . Rautenbach, CLOUDFARM: An Elastic Cloud Platform with Flexible and Adaptive Resource Management, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp.547-553
DOI : 10.1109/UCC.2014.84

[. Nikolov, M. Matousek, D. Rautenbach, L. D. Penso, J. Franz et al., Artos : System model and optimization algorithm, p.53

N. Nisan, T. Roughgarden, E. Tardos, V. Vijay, and . Vazirani, Algorithmic game theory, p.24, 2007.
DOI : 10.1017/CBO9780511800481

L. Ocl, Open data nantes : ouverture des données publiques soCloud : une plateforme multi-nuages distribuée pour la conception, le déploiement et l'exécution d'applications distribuées à large échelle, nantes. fr/, 2015. 77, 78 [ope15b] Openstack, pp.204-190, 2014.

K. Pohl, G. Böckle, J. Frank, and . Van-der-linden, Software product line engineering : foundations, principles and techniques, p.205, 2005.
DOI : 10.1007/3-540-28901-1

K. Padala, . Hou, G. Kang, X. Shin, M. Zhu et al., Automated control of multiple virtualized resources, Proceedings of the fourth ACM european conference on Computer systems, EuroSys '09, pp.13-26, 2009.
DOI : 10.1145/1519065.1519068

L. Michael and . Pinedo, Scheduling : theory, algorithms, and systems, p.161, 2012.

A. Paschke and E. Schnappinger, A categorization scheme for sla metrics. Service Oriented Electronic Commerce, pp.25-40, 2006.

]. Rao, X. Bu, C. Xu, L. Wang, and G. Yin, VCONF, Proceedings of the 6th international conference on Autonomic computing, ICAC '09, pp.137-146, 2009.
DOI : 10.1145/1555228.1555263

N. Roy, A. Dubey, and A. Gokhale, Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting, 2011 IEEE 4th International Conference on Cloud Computing, pp.500-507, 2011.
DOI : 10.1109/CLOUD.2011.42

M. Reiser, S. Stephen, and . Lavenberg, Mean-Value Analysis of Closed Multichain Queuing Networks, Journal of the ACM, vol.27, issue.2, pp.313-322, 1980.
DOI : 10.1145/322186.322195

J. Mark, . Rentmeesters, K. Wei, K. Tsai, and . Lin, A theory of lexicographic multi-criteria optimization, Engineering of Complex Computer Systems Proceedings., Second IEEE International Conference on, pp.76-79, 1996.

F. Rossi, P. Van-beek, and T. Walsh, Handbook of constraint programming, p.24, 2006.

M. Sabharwal, A. Agrawal, and G. Metri, Enabling Green IT through Energy-Aware Software, IT Professional, vol.15, issue.1, pp.19-27, 2013.
DOI : 10.1109/MITP.2012.104

E. Saxe, Power-efficient software, Communications of the ACM, vol.53, issue.2, pp.44-48, 2010.
DOI : 10.1145/1646353.1646370

S. Richard, . Sutton, G. Andrew, and . Barto, Reinforcement learning : An introduction, p.37, 1998.

C. Douglas and . Schmidt, Model-driven engineering, COMPUTER-IEEE COMPUTER SOCIETY, vol.39, issue.2, pp.25-205, 2006.

E. Henry and . Schaffer, X as a service, cloud computing, and the need for good judgment. IT professional, pp.4-5, 2009.

. Sgb-+-13-]-omran, F. Saleh, H. Gropengie?er, W. Betz, K. Mandarawi et al., Monitoring and autoscaling iaas clouds : a case for complex event processing on data streams, Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pp.387-392, 2013.

W. Shi and B. Hong, Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud, 2010 IEEE Second International Conference on Cloud Computing Technology and Science, pp.327-334, 2010.
DOI : 10.1109/CloudCom.2010.54

M. Sedaghat, F. Hernandez-rodriguez, and E. Elmroth, A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling, Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference on, CAC '13, p.58, 2015.
DOI : 10.1145/2494621.2494628

J. Schroeter, P. Mucha, M. Muth, K. Jugel, and M. Lochau, Dynamic configuration management of cloud-based applications, Proceedings of the 16th International Software Product Line Conference on, SPLC '12 -volume 1, pp.171-178, 2012.
DOI : 10.1145/2364412.2364441

E. James, R. Smith, and . Nair, The architecture of virtual machines, Computer, vol.38, issue.5, pp.32-38, 2005.

Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, CloudScale, Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC '11, pp.40-50, 2011.
DOI : 10.1145/2038916.2038921

B. Suleiman, S. Sakr, R. Jeffery, and A. Liu, On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure, Journal of Internet Services and Applications, vol.3, issue.1, pp.173-193, 2012.
DOI : 10.1007/s13174-011-0050-y

U. Sharma, P. Shenoy, and S. Sahu, A flexible elastic control plane for private clouds, Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference on, CAC '13, pp.4-72, 2013.
DOI : 10.1145/2494621.2494626

U. Sharma, P. Shenoy, S. Sahu, and A. Shaikh, A Cost-Aware Elasticity Provisioning System for the Cloud, 2011 31st International Conference on Distributed Computing Systems, pp.559-570, 2011.
DOI : 10.1109/ICDCS.2011.59

M. Turowski and A. Lenk, Vertical Scaling Capability of OpenStack, p.32, 2014.
DOI : 10.1007/978-3-319-22885-3_30

B. Urgaonkar, P. Shenoy, A. Chandra, P. Goyal, and T. Wood, Agile dynamic provisioning of multi-tier Internet applications, ACM Transactions on Autonomous and Adaptive Systems, vol.3, issue.1, pp.72-97, 2008.
DOI : 10.1145/1342171.1342172

A. Verma, P. Ahuja, and A. Neogi, pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems, pp.243-264, 2008.
DOI : 10.1109/MC.2003.1250880

A. Verma, P. Ahuja, and A. Neogi, Power-aware dynamic placement of HPC applications, Proceedings of the 22nd annual international conference on Supercomputing , ICS '08, pp.175-184, 2008.
DOI : 10.1145/1375527.1375555

S. Vinoski, Advanced Message Queuing Protocol, IEEE Internet Computing, vol.10, issue.6, pp.87-89, 2006.
DOI : 10.1109/MIC.2006.116

G. Von-laszewski, L. Wang, J. Andrew, X. Younge, and . He, Power-aware scheduling of virtual machines in DVFS-enabled clusters, 2009 IEEE International Conference on Cluster Computing and Workshops, pp.1-10, 2009.
DOI : 10.1109/CLUSTR.2009.5289182

. Vnm-+-12-]-nedeljko, D. Vasi´cvasi´c, S. Novakovi´cnovakovi´c, D. Miu?in, R. Kosti´ckosti´c et al., Dejavu : accelerating resource allocation in virtualized environments, In ACM SIGARCH Computer Architecture News, vol.40, issue.37, pp.423-436, 2012.

M. Luis, L. Vaquero, J. Rodero-merino, M. Caceres, and . Lindner, A break in the clouds : towards a cloud definition, ACM SIGCOMM Computer Communication Review, vol.39, issue.1 9, pp.50-55, 2008.

L. Wu and R. Buyya, Service Level Agreement (SLA) in Utility Computing Systems, p.15, 2012.
DOI : 10.4018/978-1-60960-794-4.ch001

J. Christopher, P. Watkins, and . Dayan, Q-learning, Machine learning, vol.8, issue.3-4, pp.279-292, 1992.

S. Weisberg, Applied linear regression, p.41, 2005.
DOI : 10.1002/0471704091

A. Weber, N. Herbst, H. Groenda, and S. Kounev, Towards a Resource Elasticity Benchmark for Cloud Environments, Proceedings of the 2nd International Workshop on Hot Topics in Cloud service Scalability, HotTopiCS '14, p.71, 2014.
DOI : 10.1145/2649563.2649571

B. Jos, . Warmer, G. Anneke, and . Kleppe, The object constraint language : Precise modeling with uml (addison-wesley object technology series). 1998. 152 [xen15a] Xen credit scheduler, pp.43-144, 1999.

C. Xu, J. Rao, and X. Bu, URL: A unified reinforcement learning approach for autonomic cloud management, Journal of Parallel and Distributed Computing, vol.72, issue.2, pp.95-105, 2012.
DOI : 10.1016/j.jpdc.2011.10.003

M. Xu, J. Zhao, R. Fortes, M. Carpenter, and . Yousif, On the Use of Fuzzy Modeling in Virtualized Data Center Management, Fourth International Conference on Autonomic Computing (ICAC'07), pp.25-25, 2007.
DOI : 10.1109/ICAC.2007.28

J. Yang, J. Qiu, and Y. Li, A Profile-Based Approach to Just-in-Time Scalability for Cloud Applications, 2009 IEEE International Conference on Cloud Computing, pp.9-16, 2009.
DOI : 10.1109/CLOUD.2009.87

Q. Zhu and G. Agrawal, Resource provisioning with budget constraints for adaptive applications in cloud environments, Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp.304-307, 2010.

B. Zeng, . Benatallah, H. Anne, M. Ngu, J. Dumas et al., Qos-aware middleware for web services composition. Software Engineering, IEEE Transactions on, vol.30, issue.5, pp.311-327, 2004.

Q. Zhang, L. Cheng, and R. Boutaba, Cloud computing: state-of-the-art and research challenges, Journal of Internet Services and Applications, vol.33, issue.4, pp.7-18, 2010.
DOI : 10.1007/s13174-010-0007-6

Y. Zhang, G. Huang, X. Liu, and H. Mei, Integrating Resource Consumption and Allocation for Infrastructure Resources on-Demand, 2010 IEEE 3rd International Conference on Cloud Computing, pp.75-82
DOI : 10.1109/CLOUD.2010.11

H. Zhao, M. Pan, and X. Liu, Xiaolin Li, and Yuguang Fang. Optimal resource rental planning for elastic applications in cloud market, Parallel & Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, pp.808-819, 2012.

X. Zhu, Z. Wang, and S. Singhal, Utility-driven workload management using nested control design, American Control Conference, pp.6-56, 2006.