,
,
, An Overview of Cloud Simulation Enhancement using the Monte-Carlo Method, Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing CC-GRID, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02161765
, Improving Cloud Simulation Using the Monte-Carlo Method, Euro-Par, vol.11014, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02161784
, Méthode pour l'étude expéri-mentale par la simulation de clouds avec SCHIaaS, In: Compas'17, 2017.
, Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed, pp.481-494, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00684943
, The Anatomy of a Large-scale Hypertextual Web Search Engine, Comput. Netw. ISDN Syst, vol.30, pp.107-117, 1998.
, ElasticSim: A Toolkit for Simulating Workflows with Cloud Resource Runtime Auto-Scaling and Stochastic Task Execution Times, J. Grid Comput, vol.15, pp.257-272, 2017.
, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and experience, vol.41, pp.23-50, 2011.
, Evaluation and Optimization of the Robustness of DAG Schedules in Heterogeneous Environments, IEEE Trans. Parallel Distrib. Syst, vol.21, pp.532-546, 2010.
, MSDA, a proteomics software suite for in-depth M ass S pectrometry D ata A nalysis using grid computing, Proteomics, vol.14, pp.1014-1019, 2014.
, Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching, Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science. CLOUDCOM '10, pp.456-463, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00758592
, Versatile, scalable, and accurate simulation of distributed applications and platforms, J. Parallel Distrib. Comput, vol.74, pp.2899-2917, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01017319
, Dynamic scaling of web applications in a virtualized cloud computing environment, 2009 IEEE International Conference on e-Business Engineering, pp.281-286, 2009.
, Mapreduce: Simplified data processing on large clusters, OSDI: Sixty Symposium on Operating System Design and Implementation, 2004.
, Bounding the project completion time distribution in PERT networks, Operations Research, vol.33, pp.862-881, 1985.
, Model-driven auto-scaling of green cloud computing infrastructure, Future Generation Computer Systems, vol.28, pp.371-378, 2012.
, A Framework for Dynamic Resource Provisioning and Adaptation in IaaS Clouds, CloudCom'11, pp.312-319, 2011.
, An updated performance comparison of virtual machines and Linux containers, 2015 IEEE International Symposium on Performance Analysis of Systems and Software, pp.171-172, 2015.
, Open mass spectrometry search algorithm, J Proteome Res, vol.3, issue.5, pp.958-964, 2004.
, Montage: a grid portal and software toolkit for science-grade astronomical image mosaicking, International Journal of Computational Science and Engineering, vol.4, pp.73-87, 2009.
, PICS: A Public IaaS Cloud Simulator, 8th IEEE International Conference on Cloud Computing, pp.211-220, 2015.
, GreenCloud: a packetlevel simulator of energy-aware cloud computing data centers, The Journal of Supercomputing, vol.62, pp.1263-1283, 2012.
, MRSG -A MapReduce Simulator over SimGrid, Parallel Computing, vol.39, pp.233-244, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00931855
, KVM -Kernel Virtual Machine
, Patterns in the Chaos -A Study of Performance Variation and Predictability in Public IaaS Clouds, ACM Trans. Internet Techn, vol.16, 2016.
, CloudScale: a novel middleware for building transparently scaling cloud applications, Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp.434-440, 2012.
, Estimating the execution time distribution for a task graph in a heterogeneous computing system, 6th Heterogeneous Computing Workshop, pp.172-184, 1997.
, MDCSim: A multi-tier data center simulation, platform, Proceedings of the 2009 IEEE International Conference on Cluster Computing, pp.1-9, 2009.
,
, A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks, Annals OR, vol.102, pp.49-64, 2001.
, Elastic Site: Using Clouds to Elastically Extend Site Resources, CCGRID'10, pp.43-52, 2010.
, e-Clouds: Scientific Computing as a Service, Complex, Intelligent, and Software Intensive Systems (CISIS), pp.481-486, 2013.
, Schlouder: A broker for IaaS clouds, Future Generation Comp. Syst, vol.69, pp.11-23, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01378219
, iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator, J. Grid Comput, vol.10, issue.1, pp.185-209, 2012.
, A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Cloud Computing -First International Conference, 2009.
, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol.34, pp.115-131, 2009.
, Multi-objective reinforcement learning for responsive grids, Journal of Grid Computing, vol.8, issue.3, pp.473-492, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00491560
, Experiences in building a mOSAIC of clouds, Journal of Cloud Computing: Advances, Systems and Applications, vol.2, p.12, 2013.
, Using Trustworthy Simulation to Engineer Cloud Schedulers, 2015 IEEE International Conference on Cloud Engineering, vol.2, pp.256-265, 2015.
,
, Understanding the performance and potential of cloud computing for scientific applications, IEEE Transactions on Cloud Computing, vol.5, pp.358-371, 2017.
, Scalr -The Hybrid Cloud Management Platform
, SCHIaaS: IaaS simulation upon SimGrid
, IaaS cloud broker for public or private clouds
, SimGrid: Versatile simulation of distributed systems
, SimSchlouder: Schlouder simulation upon SCHIaaS
, Monte Carlo Methods and the PERT Problem, issn: 0030364X, 15265463, vol.11, pp.839-860, 1963.
, IdleCached: An Idle Resource Cached Dynamic Scheduling Algorithm in Cloud Computing, UIC and ATC, pp.912-917, 2012.
, Stochastische Netzpläne und ihre Anwendung im Baubetrie, 1976.
, Distributed computing in practice: the Condor experience, Concurrency -Practice and Experience, vol.17, pp.323-356, 2005.
, Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka, Future Gener. Comput. Syst, vol.28, issue.1, pp.58-65, 2012.
, An Analysis of Provisioning and Allocation Policies for Infrastructureas-a-Service Clouds, CCGRID'12, pp.612-619, 2012.
, The Definitive Guide. 1st. O'Reilly Media, p.596521979, 2009.
, URL: A unified reinforcement learning approach for autonomic cloud management, Journal of Parallel and Distributed Computing, vol.72, pp.95-105, 2012.
, Bien que nous soyons fier de ces résultats dans le contexte d'un outil de pré-diction, nous voyons aussi les opportunités que les simulations stochastiques ouvrent en dehors de ce cas d'utilisation. Les MCS pourraient permettre l'étude précise du comportement des heuristiques de planification dans des environnements variables. En utilisant une simulation avec modèle de provisionnement stochastique, un opérateur de cloud pourrait développer et tester un algorithme de placement de VM maximisant l'efficacité énergé-tique tout en respectant ses obligations contractuelles de disponibilité, l'influence des différents paramètres sur le résultat et présente les limites de cette approche