Self-managed Cost-efficient Virtual Elastic Clusters on Hybrid Cloud Infrastructures

In this study, we describe the further development of Elastic Cloud Computing Cluster (EC3), a tool for creating self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) clouds. By using spot instances and checkpointing techniques, EC3 can significantly reduce the total execution cost as well as facilitating automatic fault tolerance. Moreover, EC3 can deploy and manage hybrid clusters across on-premises and public cloud resources, thereby introducing cloud bursting capabilities. We present the results of a case study that we conducted to assess the effectiveness of the tool based on the structural dynamic analysis of buildings. In addition, we evaluated the checkpointing algorithms in a real cloud environment with existing workloads to study their effectiveness. The results demonstrate the feasibility and benefits of this type of cluster for computationally intensive applications. © 2016 Elsevier B.V. All rights reserved.This study was supported by the program "Ayudas para la contratacion de personal investigador en formacion de caracter pre doctoral, programa VALi+d" under grant number ACIF/2013/003 from the Conselleria d'Educacio of the Generalitat Valenciana. We are also grateful for financial support received from The Spanish Ministry of Economy and Competitiveness to develop the project "CLUVIEM" under grant reference TIN2013-44390-R. Finally, we express our gratitude to D. David Ruzafa for support with the arduous task of analyzing the executions data.Calatrava Arroyo, A.; Romero Alcalde, E.; Moltó Martínez, G.; Caballer Fernández, M.; Alonso Ábalos, JM. (2016). Self-managed Cost-efficient Virtual Elastic Clusters on Hybrid Cloud Infrastructures. Future Generation Computer Systems. 61:13-25. https://doi.org/10.1016/j.future.2016.01.018S13256

Theoretical Study of Cloud Technologies

One of the main objectives of the smart city is to improve the quality of life. The information and communication technology (ICT) components are used as vital parts of the system. Increasing efficiency is the base of the smart city’s sustainability. Therefore to increase the efficiency of ICT is crucial. Although cloud technology is just one possible building block of the ICT infrastructure its theoretical study used by the smart city is important because the cloud building technologies can be extended to the use of other ICT technologies. Because of these possibilities, one should study the potential regularities of cloud operation which affects among other things, the availability, capacity, flexibility and scalability topics as well

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

[EN] Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application's computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed on a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint.The results of this work have been partially supported by ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing), funded by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154.Alfonso Laguna, CD.; Caballer Fernández, M.; Calatrava Arroyo, A.; Moltó, G.; Blanquer Espert, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures. Journal of Grid Computing. 17(1):191-204. https://doi.org/10.1007/s10723-018-9449-zS191204171Buyya, R.: High Performance Cluster Computing: Architectures and Systems. Prentice Hall PTR, Upper Saddle River (1999)de Alfonso, C., Caballer, M., Alvarruiz, F., Moltó, G.: An economic and energy-aware analysis of the viability of outsourcing cluster computing to the cloud. Futur. Gener. Comput. Syst. (Int. J. Grid Comput eScience) 29, 704–712 (2013). https://doi.org/10.1016/j.future.2012.08.014Williams, D., Jamjoom, H., Liu, Y.H., Weatherspoon, H.: Overdriver: handling memory overload in an oversubscribed cloud. ACM SIGPLAN Not. 46(7), 205 (2011). https://doi.org/10.1145/2007477.1952709 . http://dl.acm.org/citation.cfm?id=2007477.1952709Valentini, G., Lassonde, W., Khan, S., Min-Allah, N., Madani, S., Li, J., Zhang, L., Wang, L., Ghani, N., Kolodziej, J., Li, H., Zomaya, A., Xu, C.Z., Balaji, P., Vishnu, A., Pinel, F., Pecero, J., Kliazovich, D., Bouvry, P.: An overview of energy efficiency techniques in cluster computing systems. Clust. Comput. 16(1), 3–15 (2013). https://doi.org/10.1007/s10586-011-0171-xDe Alfonso, C., Caballer, M., Hernández, V.: Efficient power management in high performance computer clusters. In: Proceedings of the 1st International Multi-conference on Innovative Developments in ICT, Proceedings of the International Conference on Green Computing 2010 (ICGreen 2010), 39–44 (2010)OpenNebula: OpenNebula Cloud Software https://opennebula.org/ . [Online; accessed 12-June-2017]OpenStack: OpenStack Cloud Software. http://openstack.org . [Online; accessed 12 June 2017]VMWare: VMWare vCenter Server. https://www.vmware.com/products/vcenter-server.html . [Online; accessed 12 June 2017]De Alfonso, C., Blanquer, I.: Automatic consolidation of virtual machines in on-premises cloud platforms. In: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 1070–1079 (2017). https://doi.org/10.1109/CCGRID.2017.128Chase, J.S., Irwin, D.E., Grit, L.E., Moore, J.D., Sprenkle, S.E.: Dynamic virtual clusters in a grid site manager. In: Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing, HPDC ’03, p 90. IEEE Computer Society, Washington, DC (2003). http://dl.acm.org/citation.cfm?id=822087.823392Doelitzscher, F., Held, M., Reich, C., Sulistio, A.: Viteraas: Virtual cluster as a service. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom), pp 652–657 (2011). https://doi.org/10.1109/CloudCom.2011.101Wei, X., Wang, H., Li, H., Zou, L.: Dynamic deployment and management of elastic virtual clusters. In: 2011 Sixth Annual Chinagrid Conference (ChinaGrid), pp 35–41 (2011). https://doi.org/10.1109/ChinaGrid.2011.31de Assuncao, M.D., di Costanzo, A., Buyya, R.: Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters. In: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing, HPDC ’09, pp 141–150. ACM, New York (2009). https://doi.org/10.1145/1551609.1551635 . http://doi.acm.org/10.1145/1551609.1551635Marshall, P., Keahey, K., Freeman, T.: Elastic site: Using clouds to elastically extend site resources. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), pp 43–52 (2010). https://doi.org/10.1109/CCGRID.2010.80Niu, S., Zhai, J., Ma, X., Tang, X., Chen, W.: Cost-effective cloud hpc resource provisioning by building semi-elastic virtual clusters. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’13, pp 56:1–56:12. ACM, New York (2013). https://doi.org/10.1145/2503210.2503236 . http://doi.acm.org/10.1145/2503210.2503236Bialecki, A., Cafarella, M., Cutting, D., Omalley, O.: Hadoop: a framework for running applications on large clusters built of commodity hardware. Tech. rep. Apache Hadoop. http://hadoop.apache.org (2005)MIT: StarCluster Elastic Load Balancer. http://web.mit.edu/stardev/cluster/docs/0.92rc2/manual/load_balancer.htmlAppliance, C.C.S.: Creating elastic virtual clusters. http://cernvm.cern.ch/portal/elasticclusters (2015)Research project, T.G.: The games research project. http://www.green-datacenters.eu (2013)Cioara, T., Anghel, I., Salomie, I., Copil, G., Moldovan, D., Kipp, A.: Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning. In: 2011 10th International Symposium on Parallel and Distributed Computing (ISPDC), pp 163–169 (2011). https://doi.org/10.1109/ISPDC.2011.32Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp 500–507 (2014). https://doi.org/10.1109/PDP.2014.109Masoumzadeh, S., Hlavacs, H.: Integrating vm selection criteria in distributed dynamic vm consolidation using fuzzy q-learning. In: 2013 9th International Conference on Network and Service Management (CNSM), pp 332–338 (2013). https://doi.org/10.1109/CNSM.2013.6727854Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: 2011 12th IEEE/ACM International Conference on Grid Computing (GRID), pp 26–33 (2011). https://doi.org/10.1109/Grid.2011.13Pop, C.B., Anghel, I., Cioara, T., Salomie, I., Vartic, I.: A swarm-inspired data center consolidation methodology. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, WIMS ’12, pp 41:1–41:7. ACM, New York (2012). https://doi.org/10.1145/2254129.2254180Marzolla, M., Babaoglu, O., Panzieri, F.: Server consolidation in clouds through gossiping. In: Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WOWMOM ’11, pp 1–6. IEEE Computer Society, Washington, DC (2011). https://doi.org/10.1109/WoWMoM.2011.5986483Ghafari, S., Fazeli, M., Patooghy, A., Rikhtechi, L.: Bee-mmt: A load balancing method for power consumption management in cloud computing. In: 2013 Sixth International Conference on Contemporary Computing (IC3), pp 76–80 (2013). https://doi.org/10.1109/IC3.2013.6612165Ajiro, Y., Tanaka, A.: Improving packing algorithms for server consolidation. In: International CMG Conference, pp. 399–406. Computer Measurement Group (2007)Verma, A., Ahuja, P., Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, Middleware ’08, pp 243–264. Springer, New York (2008)Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28 (5), 755–768 (2012). https://doi.org/10.1016/j.future.2011.04.017Guazzone, M., Anglano, C., Canonico, M.: Exploiting vm migration for the automated power and performance management of green cloud computing systems. In: Proceedings of the First International Conference on Energy Efficient Data Centers, E2DC’12, pp 81–92. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-33645-4_8Shi, L., Furlong, J., Wang, R.: Empirical evaluation of vector bin packing algorithms for energy efficient data centers. In: 2013 IEEE Symposium on Computers and Communications (ISCC), pp 000,009–000,015 (2013). https://doi.org/10.1109/ISCC.2013.6754915Tomás, L., Tordsson, J.: Improving cloud infrastructure utilization through overbooking. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference on - CAC ’13, p 1. ACM Press, New York (2013). https://doi.org/10.1145/2494621.2494627Dawoud, W., Takouna, I., Meinel, C.: Elastic vm for cloud resources provisioning optimization. In: Abraham, A., Lloret Mauri, J., Buford, J., Suzuki, J., Thampi, S. (eds.) Advances in Computing and Communications, Communications in Computer and Information Science, vol. 190, pp 431–445. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-22709-7_43Tasoulas, E., Haugerund, H.R., Begnum, K.: Bayllocator: a proactive system to predict server utilization and dynamically allocate memory resources using Bayesian networks and ballooning. In: Proceedings of the 26th International Conference on Large Installation System Administration: Strategies, Tools, and Techniques, pp. 111–122. USENIX Association (2012)Hines, M.R., Gordon, A., Silva, M., Da Silva, D., Ryu, K., Ben-Yehuda, M.: Applications know best: performance-driven memory overcommit with Ginkgo. In: 2011 IEEE Third International Conference on Cloud Computing Technology and Science, pp. 130–137. IEEE. https://doi.org/10.1109/CloudCom.2011.27 (2011)Litke, A.: Manage resources on overcommitted KVM hosts. Tech. rep. IBM. http://www.ibm.com/developerworks/library/l-overcommit-kvm-resources/ (2011)De Alfonso, C., Caballer, M., Alvarruiz, F., Hernández, V.: An energy management system for cluster infrastructures. Comput. Electr. Eng. 39(8), 2579–2590 (2013). https://doi.org/10.1016/j.compeleceng.2013.05.004Moltó, G., Caballer, M, de Alfonso, C.: Automatic memory-based vertical elasticity and oversubscription on cloud platforms. Futur. Gener. Comput. Syst. 56, 1–10 (2016). https://doi.org/10.1016/j.future.2015.10.002Calatrava, A., Romero, E., Moltó, G., Caballer, M., Alonso, J.M.: Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures. Futur. Gener. Comput. Syst. 61, 13–25 (2016). https://doi.org/10.1016/j.future.2016.01.018 . http://authors.elsevier.com/sd/article/S0167739X16300024 , http://linkinghub.elsevier.com/retrieve/pii/S0167739X16300024Caballer, M., Chatziangelou, M., Calatrava, A., Moltó, G., Pérez, A.: IM integration in the EGI VMOps Dashboard. In: EGI Conference 2017 and INDIGO Summit 2017 (2017)Calatrava, A., Caballer, M., Moltó, G., Pérez, A.: Virtual Elastic Clusters in the EGI LToS with EC3. In: EGI Conference 2017 and INDIGO Summit 2017 (2017)Iosup, A., Li, H., Jan, M., Anoep, S., Dumitrescu, C., Wolters, L., Epema, D.H.: The grid workloads archive. Futur. Gener. Comput. Syst. 24(7), 672–686 (2008). https://doi.org/10.1016/j.future.2008.02.003 . http://www.sciencedirect.com/science/article/pii/S0167739X08000125Nordugrid dataset, the grid workloads archive (Online; accessed 27-March-2017). http://gwa.ewi.tudelft.nl/datasets/gwa-t-3-nordugrid/report/Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C: Dynamic Management of Virtual Infrastructures. J. Grid Comput. 13, 53–70 (2015). https://doi.org/10.1007/s10723-014-9296-5 . http://link.springer.com/article/10.1007/s10723-014-9296-

APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools

[EN] Background Scientific publications are meant to exchange knowledge among researchers but the inability to properly reproduce computational experiments limits the quality of scientific research. Furthermore, bibliography shows that irreproducible preclinical research exceeds 50%, which produces a huge waste of resources on nonprofitable research at Life Sciences field. As a consequence, scientific reproducibility is being fostered to promote Open Science through open databases and software tools that are typically deployed on existing computational resources. However, some computational experiments require complex virtual infrastructures, such as elastic clusters of PCs, that can be dynamically provided from multiple clouds. Obtaining these infrastructures requires not only an infrastructure provider, but also advanced knowledge in the cloud computing field. Objectives The main aim of this paper is to improve reproducibility in life sciences to produce better and more cost-effective research. For that purpose, our intention is to simplify the infrastructure usage and deployment for researchers. Methods This paper introduces Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open source extension for Jupyter to deploy deterministic virtual infrastructures across multiclouds for reproducible scientific computational experiments. To exemplify its utilization and how APRICOT can improve the reproduction of experiments with complex computation requirements, two examples in the field of life sciences are provided. All requirements to reproduce both experiments are disclosed within APRICOT and, therefore, can be reproduced by the users. Results To show the capabilities of APRICOT, we have processed a real magnetic resonance image to accurately characterize a prostate cancer using a Message Passing Interface cluster deployed automatically with APRICOT. In addition, the second example shows how APRICOT scales the deployed infrastructure, according to the workload, using a batch cluster. This example consists of a multiparametric study of a positron emission tomography image reconstruction. Conclusion APRICOT's benefits are the integration of specific infrastructure deployment, the management and usage for Open Science, making experiments that involve specific computational infrastructures reproducible. All the experiment steps and details can be documented at the same Jupyter notebook which includes infrastructure specifications, data storage, experimentation execution, results gathering, and infrastructure termination. Thus, distributing the experimentation notebook and needed data should be enough to reproduce the experiment.This study was supported by the program "Ayudas para la contratación de personal investigador en formación de carácter predoctoral, programa VALi+d" under grant number ACIF/2018/148 from the Conselleria d'Educació of the Generalitat Valenciana and the "Fondo Social Europeo" (FSE). The authors would like to thank the Spanish "Ministerio de Economía, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R and the European Commission, Horizon 2020 grant agreement No 826494 (PRIMAGE). The MRI prostate study case used in this article has been retrospectively collected from a project of prostate MRI biomarkers validation.Giménez-Alventosa, V.; Segrelles Quilis, JD.; Moltó, G.; Roca-Sogorb, M. (2020). APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools. Methods of Information in Medicine. 59(S 02):e33-e45. https://doi.org/10.1055/s-0040-1712460Se33e4559S 02Donoho, D. L., Maleki, A., Rahman, I. U., Shahram, M., & Stodden, V. (2009). Reproducible Research in Computational Harmonic Analysis. Computing in Science & Engineering, 11(1), 8-18. doi:10.1109/mcse.2009.15Freedman, L. P., Cockburn, I. M., & Simcoe, T. S. (2015). The Economics of Reproducibility in Preclinical Research. PLOS Biology, 13(6), e1002165. doi:10.1371/journal.pbio.1002165Chillarón, M., Vidal, V., & Verdú, G. (2020). CT image reconstruction with SuiteSparseQR factorization package. Radiation Physics and Chemistry, 167, 108289. doi:10.1016/j.radphyschem.2019.04.039Reader, A. J., Ally, S., Bakatselos, F., Manavaki, R., Walledge, R. J., Jeavons, A. P., … Zweit, J. (2002). One-pass list-mode EM algorithm for high-resolution 3-D PET image reconstruction into large arrays. IEEE Transactions on Nuclear Science, 49(3), 693-699. doi:10.1109/tns.2002.1039550Giménez-Alventosa, V., Antunes, P. C. G., Vijande, J., Ballester, F., Pérez-Calatayud, J., & Andreo, P. (2016). Collision-kerma conversion between dose-to-tissue and dose-to-water by photon energy-fluence corrections in low-energy brachytherapy. Physics in Medicine and Biology, 62(1), 146-164. doi:10.1088/1361-6560/aa4f6aWilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Bourne, P. E. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1). doi:10.1038/sdata.2016.18Calatrava, A., Romero, E., Moltó, G., Caballer, M., & Alonso, J. M. (2016). Self-managed cost-efficient virtual elastic clusters on hybrid Cloud infrastructures. Future Generation Computer Systems, 61, 13-25. doi:10.1016/j.future.2016.01.018Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5Wolstencroft, K., Owen, S., Krebs, O., Nguyen, Q., Stanford, N. J., Golebiewski, M., … Goble, C. (2015). SEEK: a systems biology data and model management platform. BMC Systems Biology, 9(1). doi:10.1186/s12918-015-0174-yDe Alfonso, C., Caballer, M., Calatrava, A., Moltó, G., & Blanquer, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures. Journal of Grid Computing, 17(1), 191-204. doi:10.1007/s10723-018-9449-zRawla, P. (2019). Epidemiology of Prostate Cancer. World Journal of Oncology, 10(2), 63-89. doi:10.14740/wjon1191Bratan, F., Niaf, E., Melodelima, C., Chesnais, A. L., Souchon, R., Mège-Lechevallier, F., … Rouvière, O. (2013). Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study. European Radiology, 23(7), 2019-2029. doi:10.1007/s00330-013-2795-0Le, J. D., Tan, N., Shkolyar, E., Lu, D. Y., Kwan, L., Marks, L. S., … Reiter, R. E. (2015). Multifocality and Prostate Cancer Detection by Multiparametric Magnetic Resonance Imaging: Correlation with Whole-mount Histopathology. European Urology, 67(3), 569-576. doi:10.1016/j.eururo.2014.08.079Brix, G., Semmler, W., Port, R., Schad, L. R., Layer, G., & Lorenz, W. J. (1991). Pharmacokinetic Parameters in CNS Gd-DTPA Enhanced MR Imaging. Journal of Computer Assisted Tomography, 15(4), 621-628. doi:10.1097/00004728-199107000-00018Larsson, H. B. W., Stubgaard, M., Frederiksen, J. L., Jensen, M., Henriksen, O., & Paulson, O. B. (1990). Quantitation of blood-brain barrier defect by magnetic resonance imaging and gadolinium-DTPA in patients with multiple sclerosis and brain tumors. Magnetic Resonance in Medicine, 16(1), 117-131. doi:10.1002/mrm.1910160111Tofts, P. S., & Kermode, A. G. (1991). Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic Resonance in Medicine, 17(2), 357-367. doi:10.1002/mrm.1910170208Donahue, K. M., Weisskoff, R. M., & Burstein, D. (1997). Water diffusion and exchange as they influence contrast enhancement. Journal of Magnetic Resonance Imaging, 7(1), 102-110. doi:10.1002/jmri.1880070114Flouri, D., Lesnic, D., & Sourbron, S. P. (2015). Fitting the two-compartment model in DCE-MRI by linear inversion. Magnetic Resonance in Medicine, 76(3), 998-1006. doi:10.1002/mrm.25991Brun, R., & Rademakers, F. (1997). ROOT — An object oriented data analysis framework. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 389(1-2), 81-86. doi:10.1016/s0168-9002(97)00048-xXuan Liu, Comtat, C., Michel, C., Kinahan, P., Defrise, M., & Townsend, D. (2001). Comparison of 3-D reconstruction with 3D-OSEM and with FORE+OSEM for PET. IEEE Transactions on Medical Imaging, 20(8), 804-814. doi:10.1109/42.938248Singh, S., Kalra, M. K., Hsieh, J., Licato, P. E., Do, S., Pien, H. H., & Blake, M. A. (2010). Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques. Radiology, 257(2), 373-383. doi:10.1148/radiol.10092212Shepp, L. A., & Vardi, Y. (1982). Maximum Likelihood Reconstruction for Emission Tomography. IEEE Transactions on Medical Imaging, 1(2), 113-122. doi:10.1109/tmi.1982.4307558Goo, J. M., Tongdee, T., Tongdee, R., Yeo, K., Hildebolt, C. F., & Bae, K. T. (2005). Volumetric Measurement of Synthetic Lung Nodules with Multi–Detector Row CT: Effect of Various Image Reconstruction Parameters and Segmentation Thresholds on Measurement Accuracy. Radiology, 235(3), 850-856. doi:10.1148/radiol.2353040737Ravenel, J. G., Leue, W. M., Nietert, P. J., Miller, J. V., Taylor, K. K., & Silvestri, G. A. (2008). Pulmonary Nodule Volume: Effects of Reconstruction Parameters on Automated Measurements—A Phantom Study. Radiology, 247(2), 400-408. doi:10.1148/radiol.2472070868Hu, Y.-H., Zhao, B., & Zhao, W. (2008). Image artifacts in digital breast tomosynthesis: Investigation of the effects of system geometry and reconstruction parameters using a linear system approach. Medical Physics, 35(12), 5242-5252. doi:10.1118/1.2996110Lyra, M., & Ploussi, A. (2011). Filtering in SPECT Image Reconstruction. International Journal of Biomedical Imaging, 2011, 1-14. doi:10.1155/2011/69379

A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees

[EN] This article describes the development of an automated configuration of a software platform for Data Analytics that supports horizontal and vertical elasticity to guarantee meeting a specific deadline. It specifies all the components, software dependencies and configurations required to build up the cluster, and analyses the deployment times of different instances, as well as the horizontal and vertical elasticity. The approach followed builds up self-managed hybrid clusters that can deal with different workloads and network requirements. The article describes the structure of the recipes, points out to public repositories where the code is available and discusses the limitations of the approach as well as the results of several experiments.The work presented in this article has been partially funded by a research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modelling, Simulation and early Detection of diseases in Personalized Medicine). The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R.López-Huguet, S.; Pérez-González, AM.; Calatrava Arroyo, A.; Alfonso Laguna, CD.; Caballer Fernández, M.; Moltó, G.; Blanquer Espert, I. (2019). A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees. Future Generation Computer Systems. 96:449-461. https://doi.org/10.1016/j.future.2019.02.047S4494619

Serverless Computing Strategies on Cloud Platforms

[ES] Con el desarrollo de la Computación en la Nube, la entrega de recursos virtualizados a través de Internet ha crecido enormemente en los últimos años. Las Funciones como servicio (FaaS), uno de los modelos de servicio más nuevos dentro de la Computación en la Nube, permite el desarrollo e implementación de aplicaciones basadas en eventos que cubren servicios administrados en Nubes públicas y locales. Los proveedores públicos de Computación en la Nube adoptan el modelo FaaS dentro de su catálogo para proporcionar computación basada en eventos altamente escalable para las aplicaciones. Por un lado, los desarrolladores especializados en esta tecnología se centran en crear marcos de código abierto serverless para evitar el bloqueo con los proveedores de la Nube pública. A pesar del desarrollo logrado por la informática serverless, actualmente hay campos relacionados con el procesamiento de datos y la optimización del rendimiento en la ejecución en los que no se ha explorado todo el potencial. En esta tesis doctoral se definen tres estrategias de computación serverless que permiten evidenciar los beneficios de esta tecnología para el procesamiento de datos. Las estrategias implementadas permiten el análisis de datos con la integración de dispositivos de aceleración para la ejecución eficiente de aplicaciones científicas en plataformas cloud públicas y locales. En primer lugar, se desarrolló la plataforma CloudTrail-Tracker. CloudTrail-Tracker es una plataforma serverless de código abierto basada en eventos para el procesamiento de datos que puede escalar automáticamente hacia arriba y hacia abajo, con la capacidad de escalar a cero para minimizar los costos operativos. Seguidamente, se plantea la integración de GPUs en una plataforma serverless local impulsada por eventos para el procesamiento de datos escalables. La plataforma admite la ejecución de aplicaciones como funciones severless en respuesta a la carga de un archivo en un sistema de almacenamiento de ficheros, lo que permite la ejecución en paralelo de las aplicaciones según los recursos disponibles. Este procesamiento es administrado por un cluster Kubernetes elástico que crece y decrece automáticamente según las necesidades de procesamiento. Ciertos enfoques basados en tecnologías de virtualización de GPU como rCUDA y NVIDIA-Docker se evalúan para acelerar el tiempo de ejecución de las funciones. Finalmente, se implementa otra solución basada en el modelo serverless para ejecutar la fase de inferencia de modelos de aprendizaje automático previamente entrenados, en la plataforma de Amazon Web Services y en una plataforma privada con el framework OSCAR. El sistema crece elásticamente de acuerdo con la demanda y presenta una escalado a cero para minimizar los costes. Por otra parte, el front-end proporciona al usuario una experiencia simplificada en la obtención de la predicción de modelos de aprendizaje automático. Para demostrar las funcionalidades y ventajas de las soluciones propuestas durante esta tesis se recogen varios casos de estudio que abarcan diferentes campos del conocimiento como la analítica de aprendizaje y la Inteligencia Artificial. Esto demuestra que la gama de aplicaciones donde la computación serverless puede aportar grandes beneficios es muy amplia. Los resultados obtenidos avalan el uso del modelo serverless en la simplificación del diseño de arquitecturas para el uso intensivo de datos en aplicaciones complejas.[CA] Amb el desenvolupament de la Computació en el Núvol, el lliurament de recursos virtualitzats a través d'Internet ha crescut granment en els últims anys. Les Funcions com a Servei (FaaS), un dels models de servei més nous dins de la Computació en el Núvol, permet el desenvolupament i implementació d'aplicacions basades en esdeveniments que cobreixen serveis administrats en Núvols públics i locals. Els proveïdors de computació en el Núvol públic adopten el model FaaS dins del seu catàleg per a proporcionar a les aplicacions computació altament escalable basada en esdeveniments. D'una banda, els desenvolupadors especialitzats en aquesta tecnologia se centren en crear marcs de codi obert serverless per a evitar el bloqueig amb els proveïdors del Núvol públic. Malgrat el desenvolupament alcançat per la informàtica serverless, actualment hi ha camps relacionats amb el processament de dades i l'optimització del rendiment d'execució en els quals no s'ha explorat tot el potencial. En aquesta tesi doctoral es defineixen tres estratègies informàtiques serverless que permeten demostrar els beneficis d'aquesta tecnologia per al processament de dades. Les estratègies implementades permeten l'anàlisi de dades amb a integració de dispositius accelerats per a l'execució eficient d'aplicacion scientífiques en plataformes de Núvol públiques i locals. En primer lloc, es va desenvolupar la plataforma CloudTrail-Tracker. CloudTrail-Tracker és una plataforma de codi obert basada en esdeveniments per al processament de dades serverless que pot escalar automáticament cap amunt i cap avall, amb la capacitat d'escalar a zero per a minimitzar els costos operatius. A continuació es planteja la integració de GPUs en una plataforma serverless local impulsada per esdeveniments per al processament de dades escalables. La plataforma admet l'execució d'aplicacions com funcions severless en resposta a la càrrega d'un arxiu en un sistema d'emmagatzemaments de fitxers, la qual cosa permet l'execució en paral·lel de les aplicacions segon sels recursos disponibles. Este processament és administrat per un cluster Kubernetes elàstic que creix i decreix automàticament segons les necessitats de processament. Certs enfocaments basats en tecnologies de virtualització de GPU com rCUDA i NVIDIA-Docker s'avaluen per a accelerar el temps d'execució de les funcions. Finalment s'implementa una altra solució basada en el model serverless per a executar la fase d'inferència de models d'aprenentatge automàtic prèviament entrenats en la plataforma de Amazon Web Services i en una plataforma privada amb el framework OSCAR. El sistema creix elàsticament d'acord amb la demanda i presenta una escalada a zero per a minimitzar els costos. D'altra banda el front-end proporciona a l'usuari una experiència simplificada en l'obtenció de la predicció de models d'aprenentatge automàtic. Per a demostrar les funcionalitats i avantatges de les solucions proposades durant esta tesi s'arrepleguen diversos casos d'estudi que comprenen diferents camps del coneixement com l'analítica d'aprenentatge i la Intel·ligència Artificial. Això demostra que la gamma d'aplicacions on la computació serverless pot aportar grans beneficis és molt àmplia. Els resultats obtinguts avalen l'ús del model serverless en la simplificació del disseny d'arquitectures per a l'ús intensiu de dades en aplicacions complexes.[EN] With the development of Cloud Computing, the delivery of virtualized resources over the Internet has greatly grown in recent years. Functions as a Service (FaaS), one of the newest service models within Cloud Computing, allows the development and implementation of event-based applications that cover managed services in public and on-premises Clouds. Public Cloud Computing providers adopt the FaaS model within their catalog to provide event-driven highly-scalable computing for applications. On the one hand, developers specialized in this technology focus on creating open-source serverless frameworks to avoid the lock-in with public Cloud providers. Despite the development achieved by serverless computing, there are currently fields related to data processing and execution performance optimization where the full potential has not been explored. In this doctoral thesis three serverless computing strategies are defined that allow to demonstrate the benefits of this technology for data processing. The implemented strategies allow the analysis of data with the integration of accelerated devices for the efficient execution of scientific applications on public and on-premises Cloud platforms. Firstly, the CloudTrail-Tracker platform was developed to extract and process learning analytics in the Cloud. CloudTrail-Tracker is an event-driven open-source platform for serverless data processing that can automatically scale up and down, featuring the ability to scale to zero for minimizing the operational costs. Next, the integration of GPUs in an event-driven on-premises serverless platform for scalable data processing is discussed. The platform supports the execution of applications as serverless functions in response to the loading of a file in a file storage system, which allows the parallel execution of applications according to available resources. This processing is managed by an elastic Kubernetes cluster that automatically grows and shrinks according to the processing needs. Certain approaches based on GPU virtualization technologies such as rCUDA and NVIDIA-Docker are evaluated to speed up the execution time of the functions. Finally, another solution based on the serverless model is implemented to run the inference phase of previously trained machine learning models on theAmazon Web Services platform and in a private platform with the OSCAR framework. The system grows elastically according to demand and is scaled to zero to minimize costs. On the other hand, the front-end provides the user with a simplified experience in obtaining the prediction of machine learning models. To demonstrate the functionalities and advantages of the solutions proposed during this thesis, several case studies are collected covering different fields of knowledge such as learning analytics and Artificial Intelligence. This shows the wide range of applications where serverless computing can bring great benefits. The results obtained endorse the use of the serverless model in simplifying the design of architectures for the intensive data processing in complex applications.Naranjo Delgado, DM. (2021). Serverless Computing Strategies on Cloud Platforms [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160916TESI

Elastic, Interoperable and Container-based Cloud Infrastructures for High Performance Computing

Tesis por compendio[ES] Las aplicaciones científicas implican generalmente una carga computacional variable y no predecible a la que las instituciones deben hacer frente variando dinámicamente la asignación de recursos en función de las distintas necesidades computacionales. Las aplicaciones científicas pueden necesitar grandes requisitos. Por ejemplo, una gran cantidad de recursos computacionales para el procesado de numerosos trabajos independientes (High Throughput Computing o HTC) o recursos de alto rendimiento para la resolución de un problema individual (High Performance Computing o HPC). Los recursos computacionales necesarios en este tipo de aplicaciones suelen acarrear un coste muy alto que puede exceder la disponibilidad de los recursos de la institución o estos pueden no adaptarse correctamente a las necesidades de las aplicaciones científicas, especialmente en el caso de infraestructuras preparadas para la ejecución de aplicaciones de HPC. De hecho, es posible que las diferentes partes de una aplicación necesiten distintos tipos de recursos computacionales. Actualmente las plataformas de servicios en la nube se han convertido en una solución eficiente para satisfacer la demanda de las aplicaciones HTC, ya que proporcionan un abanico de recursos computacionales accesibles bajo demanda. Por esta razón, se ha producido un incremento en la cantidad de clouds híbridos, los cuales son una combinación de infraestructuras alojadas en servicios en la nube y en las propias instituciones (on-premise). Dado que las aplicaciones pueden ser procesadas en distintas infraestructuras, actualmente la portabilidad de las aplicaciones se ha convertido en un aspecto clave. Probablemente, las tecnologías de contenedores son la tecnología más popular para la entrega de aplicaciones gracias a que permiten reproducibilidad, trazabilidad, versionado, aislamiento y portabilidad. El objetivo de la tesis es proporcionar una arquitectura y una serie de servicios para proveer infraestructuras elásticas híbridas de procesamiento que puedan dar respuesta a las diferentes cargas de trabajo. Para ello, se ha considerado la utilización de elasticidad vertical y horizontal desarrollando una prueba de concepto para proporcionar elasticidad vertical y se ha diseñado una arquitectura cloud elástica de procesamiento de Análisis de Datos. Después, se ha trabajo en una arquitectura cloud de recursos heterogéneos de procesamiento de imágenes médicas que proporciona distintas colas de procesamiento para trabajos con diferentes requisitos. Esta arquitectura ha estado enmarcada en una colaboración con la empresa QUIBIM. En la última parte de la tesis, se ha evolucionado esta arquitectura para diseñar e implementar un cloud elástico, multi-site y multi-tenant para el procesamiento de imágenes médicas en el marco del proyecto europeo PRIMAGE. Esta arquitectura utiliza un almacenamiento distribuido integrando servicios externos para la autenticación y la autorización basados en OpenID Connect (OIDC). Para ello, se ha desarrollado la herramienta kube-authorizer que, de manera automatizada y a partir de la información obtenida en el proceso de autenticación, proporciona el control de acceso a los recursos de la infraestructura de procesamiento mediante la creación de las políticas y roles. Finalmente, se ha desarrollado otra herramienta, hpc-connector, que permite la integración de infraestructuras de procesamiento HPC en infraestructuras cloud sin necesitar realizar cambios en la infraestructura HPC ni en la arquitectura cloud. Cabe destacar que, durante la realización de esta tesis, se han utilizado distintas tecnologías de gestión de trabajos y de contenedores de código abierto, se han desarrollado herramientas y componentes de código abierto y se han implementado recetas para la configuración automatizada de las distintas arquitecturas diseñadas desde la perspectiva DevOps.[CA] Les aplicacions científiques impliquen generalment una càrrega computacional variable i no predictible a què les institucions han de fer front variant dinàmicament l'assignació de recursos en funció de les diferents necessitats computacionals. Les aplicacions científiques poden necessitar grans requisits. Per exemple, una gran quantitat de recursos computacionals per al processament de nombrosos treballs independents (High Throughput Computing o HTC) o recursos d'alt rendiment per a la resolució d'un problema individual (High Performance Computing o HPC). Els recursos computacionals necessaris en aquest tipus d'aplicacions solen comportar un cost molt elevat que pot excedir la disponibilitat dels recursos de la institució o aquests poden no adaptar-se correctament a les necessitats de les aplicacions científiques, especialment en el cas d'infraestructures preparades per a l'avaluació d'aplicacions d'HPC. De fet, és possible que les diferents parts d'una aplicació necessiten diferents tipus de recursos computacionals. Actualment les plataformes de servicis al núvol han esdevingut una solució eficient per satisfer la demanda de les aplicacions HTC, ja que proporcionen un ventall de recursos computacionals accessibles a demanda. Per aquest motiu, s'ha produït un increment de la quantitat de clouds híbrids, els quals són una combinació d'infraestructures allotjades a servicis en el núvol i a les mateixes institucions (on-premise). Donat que les aplicacions poden ser processades en diferents infraestructures, actualment la portabilitat de les aplicacions s'ha convertit en un aspecte clau. Probablement, les tecnologies de contenidors són la tecnologia més popular per a l'entrega d'aplicacions gràcies al fet que permeten reproductibilitat, traçabilitat, versionat, aïllament i portabilitat. L'objectiu de la tesi és proporcionar una arquitectura i una sèrie de servicis per proveir infraestructures elàstiques híbrides de processament que puguen donar resposta a les diferents càrregues de treball. Per a això, s'ha considerat la utilització d'elasticitat vertical i horitzontal desenvolupant una prova de concepte per proporcionar elasticitat vertical i s'ha dissenyat una arquitectura cloud elàstica de processament d'Anàlisi de Dades. Després, s'ha treballat en una arquitectura cloud de recursos heterogenis de processament d'imatges mèdiques que proporciona distintes cues de processament per a treballs amb diferents requisits. Aquesta arquitectura ha estat emmarcada en una col·laboració amb l'empresa QUIBIM. En l'última part de la tesi, s'ha evolucionat aquesta arquitectura per dissenyar i implementar un cloud elàstic, multi-site i multi-tenant per al processament d'imatges mèdiques en el marc del projecte europeu PRIMAGE. Aquesta arquitectura utilitza un emmagatzemament integrant servicis externs per a l'autenticació i autorització basats en OpenID Connect (OIDC). Per a això, s'ha desenvolupat la ferramenta kube-authorizer que, de manera automatitzada i a partir de la informació obtinguda en el procés d'autenticació, proporciona el control d'accés als recursos de la infraestructura de processament mitjançant la creació de les polítiques i rols. Finalment, s'ha desenvolupat una altra ferramenta, hpc-connector, que permet la integració d'infraestructures de processament HPC en infraestructures cloud sense necessitat de realitzar canvis en la infraestructura HPC ni en l'arquitectura cloud. Es pot destacar que, durant la realització d'aquesta tesi, s'han utilitzat diferents tecnologies de gestió de treballs i de contenidors de codi obert, s'han desenvolupat ferramentes i components de codi obert, i s'han implementat receptes per a la configuració automatitzada de les distintes arquitectures dissenyades des de la perspectiva DevOps.[EN] Scientific applications generally imply a variable and an unpredictable computational workload that institutions must address by dynamically adjusting the allocation of resources to their different computational needs. Scientific applications could require a high capacity, e.g. the concurrent usage of computational resources for processing several independent jobs (High Throughput Computing or HTC) or a high capability by means of using high-performance resources for solving complex problems (High Performance Computing or HPC). The computational resources required in this type of applications usually have a very high cost that may exceed the availability of the institution's resources or they are may not be successfully adapted to the scientific applications, especially in the case of infrastructures prepared for the execution of HPC applications. Indeed, it is possible that the different parts that compose an application require different type of computational resources. Nowadays, cloud service platforms have become an efficient solution to meet the need of HTC applications as they provide a wide range of computing resources accessible on demand. For this reason, the number of hybrid computational infrastructures has increased during the last years. The hybrid computation infrastructures are the combination of infrastructures hosted in cloud platforms and the computation resources hosted in the institutions, which are named on-premise infrastructures. As scientific applications can be processed on different infrastructures, the application delivery has become a key issue. Nowadays, containers are probably the most popular technology for application delivery as they ease reproducibility, traceability, versioning, isolation, and portability. The main objective of this thesis is to provide an architecture and a set of services to build up hybrid processing infrastructures that fit the need of different workloads. Hence, the thesis considered aspects such as elasticity and federation. The use of vertical and horizontal elasticity by developing a proof of concept to provide vertical elasticity on top of an elastic cloud architecture for data analytics. Afterwards, an elastic cloud architecture comprising heterogeneous computational resources has been implemented for medical imaging processing using multiple processing queues for jobs with different requirements. The development of this architecture has been framed in a collaboration with a company called QUIBIM. In the last part of the thesis, the previous work has been evolved to design and implement an elastic, multi-site and multi-tenant cloud architecture for medical image processing has been designed in the framework of a European project PRIMAGE. This architecture uses a storage integrating external services for the authentication and authorization based on OpenID Connect (OIDC). The tool kube-authorizer has been developed to provide access control to the resources of the processing infrastructure in an automatic way from the information obtained in the authentication process, by creating policies and roles. Finally, another tool, hpc-connector, has been developed to enable the integration of HPC processing infrastructures into cloud infrastructures without requiring modifications in both infrastructures, cloud and HPC. It should be noted that, during the realization of this thesis, different contributions to open source container and job management technologies have been performed by developing open source tools and components and configuration recipes for the automated configuration of the different architectures designed from the DevOps perspective. The results obtained support the feasibility of the vertical elasticity combined with the horizontal elasticity to implement QoS policies based on a deadline, as well as the feasibility of the federated authentication model to combine public and on-premise clouds.López Huguet, S. (2021). Elastic, Interoperable and Container-based Cloud Infrastructures for High Performance Computing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172327TESISCompendi

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. Because of this, there are still several aspects that deserve additional research for finding optimal adaptability strategies. Those open issues are also discussed.This work has been partially supported by EU FEDER and Spanish MINECO under research Grant TIN2012-37719-C03-01.Muñoz-Escoí, FD.; Bernabeu Aubán, JM. (2017). A survey on elasticity management in PaaS systems. Computing. 99(7):617-656. https://doi.org/10.1007/s00607-016-0507-8S617656997Ajmani S (2004) Automatic software upgrades for distributed systems. PhD thesis, Department of Electrical and Computer Science, Massachusetts Institute of Technology, USAAjmani S, Liskov B, Shrira L (2006) Modular software upgrades for distributed systems. In: 20th European Conference on Object-Oriented Programming (ECOOP), Nantes, France, pp 452–476Alhamad M, Dillon TS, Chang E (2010) Conceptual SLA framework for cloud computing. In: 4th International Conference on Digital Ecosystems and Technologies (DEST), Dubai, pp 606–610Almeida S, Leitão J, Rodrigues LET (2013) ChainReaction: a causal+ consistent datastore based on chain replication. In: 8th EuroSys Conference, Prague, Czech Republic, pp 85–98Araujo J, Matos R, Maciel PRM, Matias R (2011) Software aging issues on the Eucalyptus cloud computing infrastructure. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, Alaska, USA, pp 1411–1416Arief LB, Speirs NA (2000) A UML tool for an automatic generation of simulation programs. In: Worshop on Software and Performance (WOSP), Ottawa, Canada, pp 71–76Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58Bailis P, Ghodsi A (2013) Eventual consistency today: limitations, extensions, and beyond. Commun ACM 56(5):55–63Bailis P, Ghodsi A, Hellerstein JM, Stoica I (2013) Bolt-on causal consistency. In: Intnl Conf Mgmnt Data (SIGMOD). NY, USA, New York, pp 761–772Balsamo S, Marco AD, Inverardi P, Simeoni M (2004) Model-based performance prediction in software development: a survey. IEEE Trans Softw Eng 30(5):295–310Barham P, Dragovic B, Fraser K, Hand S, Harris TL, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. In: 19th ACM Symposium on Operating Systems Principles (SOSP), Bolton Landing, NY, USA, pp 164–177Bennani MN, Menascé DA (2005) Resource allocation for autonomic data centers using analytic performance models. In: 2nd Intnl Conf Auton Comput (ICAC), Seattle, WA, USA, pp 229–240Birman KP (1996) Building Secure and Reliable Network Applications. Manning Publications Co., ISBN 1-884777-29-5Bloom T (1983) Dynamic module replacement in a distributed programming system. PhD thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USABloom T, Day M (1993) Reconfiguration and module replacement in Argus: theory and practice. Softw Eng J 8(2):102–108Caballer M, Segrelles Quilis JD, Moltó G, Blanquer I (2015) A platform to deploy customized scientific virtual infrastructures on the cloud. Concurr Comput Pract E 27(16):4318–4329Calatrava A, Romero E, Moltó G, Caballer M, Alonso JM (2016) Self-managed cost-efficient virtual elastic clusters on hybrid cloud infrastructures. Future Gener Comp Syst 61:13–25Calcavecchia NM, Caprarescu BA, Nitto ED, Dubois DJ, Petcu D (2012) DEPAS: a decentralized probabilistic algorithm for auto-scaling. Computing 94(8–10):701–730Casalicchio E, Silvestri L (2013) Mechanisms for SLA provisioning in cloud-based service providers. Comput Netw 57(3):795–810Casalicchio E, Menascé DA, Aldhalaan A (2013) Autonomic resource provisioning in cloud systems with availability goals. In: ACM Cloud Autonomic Computing Conference (CAC), FL, USA, Miami, pp 1–10Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):4Copil G, Trihinas D, Truong HL, Moldovan D, Pallis G, Dustdar S, Dikaiakos MD (2014) ADVISE—A framework for evaluating cloud service elasticity behavior. In: 12th International Conference on Service-Oriented Computing (ICSOC), France, Paris, pp 275–290Cotroneo D, Natella R, Pietrantuono R, Russo S (2014) A survey of software aging and rejuvenation studies. ACM J Emerg Technol 10(1):8:1–8:34Coutinho EF, de Carvalho Sousa FR, Rego PAL, Gomes DG, de Souza JN (2015) Elasticity in cloud computing: a survey. Ann Telecommun 70(15):289–309Dawoud W, Takouna I, Meinel C (2011) Elastic VM for cloud resources provisioning optimization. In: 1st International Conference on Advances in Computing and Communications (ACC), Kochi, India, pp 431–445de Juan-Marín R, Decker H, Armendáriz-Íñigo JE, Bernabéu-Aubán JM, Muñoz-EscoíFD (2015) Scalability approaches for causal multicast: a survey. Computing (in press)de Miguel M, Lambolais T, Hannouz M, Betgé-Brezetz S, Piekarec S (2000) UML extensions for the specification and evaluation of latency constraints in architectural models. In: Workshop on Software and Performance (WOSP), Ottawa, Canada, pp 83–88Demers AJ, Greene DH, Hauser C, Irish W, Larson J, Shenker S, Sturgis HE, Swinehart DC, Terry DB (1987) Epidemic algorithms for replicated database maintenance. In: 6th ACM Symposium on Principles of Distributed Computing (PODC), Vancouver, Canada, pp 1–12Dustdar S, Guo Y, Satzger B, Truong HL (2011) Principles of elastic processes. IEEE Internet Comput 15(5):66–71Emeakaroha VC, Brandic I, Maurer M, Dustdar S (2013) Cloud resource provisioning and SLA enforcement via LoM2HiS framework. Concurr Comput Pract E 25(10):1462–1481Felter W, Ferreira A, Rajamony R, Rubio J (2015) An updated performance comparison of virtual machines and Linux containers. In: IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Philadelphia, PA, USA, pp 171–172Fox A, Brewer EA (1999) Harvest, yield and scalable tolerant systems. In: 7th Workshop on Hot Topics in Operating Systems (HotOS), Rio Rico, Arizona, USA, pp 174–178Galante G, De Bona LCE (2012) A survey on cloud computing elasticity. In: 5th International Conference on Utility and Cloud Computing (UCC), Chicago, IL, USA, pp 263–270Galante G, De Bona LCE, Mury AR, Schulze B, Righi RR (2016) An analysis of public clouds elasticity in the execution of scientific applications: a survey. J Grid Comput 14(2):193–216Gambi A, Hummer W, Truong HL, Dustdar S (2013) Testing elastic computing systems. IEEE Internet Comput 17(6):76–82Garg S, van Moorsel APA, Vaidyanathan K, Trivedi KS (1998) A methodology for detection and estimation of software aging. In: 9th International Symposium on Software Reliability Engineering (ISSRE), Paderborn, Germany, pp 283–292Gey F, Landuyt DV, Joosen W (2015) Middleware for customizable multi-staged dynamic upgrades of multi-tenant SaaS applications. In: 8th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), Limassol, Cyprus, pp 102–111Gilbert S, Lynch NA (2002) Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2):51–59Gong Z, Gu X, Wilkes J (2010) PRESS: PRedictive Elastic reSource Scaling for cloud systems. In: 6th International Conference on Network and Service Management (CNSM), Niagara Falls, Canada, pp 9–16Grozev N, Buyya R (2014) Inter-cloud architectures and application brokering: taxonomy and survey. Softw Pract Exp 44(3):369–390Hammer M (2009) How to touch a running system. reconfiguration of stateful components. PhD thesis, Facultät für Mathematik, Informatik und Statistik, Ludwig-Maximilians-Universität München, Munich, GermanyHasan MZ, Magana E, Clemm A, Tucker L, Gudreddi SLD (2012) Integrated and autonomic cloud resource scaling. In: IEEE Network Operations and Management Symposium (NOMS), Maui, HI, USA, pp 1327–1334Herbst NR, Kounev S, Reussner R (2013) Elasticity in cloud computing: What it is, and what it is not. In: 10th International Conference on Autonomic Computing (ICAC), San Jose, CA, USA, pp 23–27Hermanns H, Herzog U, Katoen J (2002) Process algebra for performance evaluation. Theor Comput Sci 274(1–2):43–87Horn P (2001) Autonomic computing: IBM’s perspective on the state of information technology. Tech. rep. IBM PressHuebscher MC, McCann JA (2008) A survey of autonomic computing—degrees, models, and applications. ACM Comput Surv 40(3):7Hwang J, Zeng S, Wu F, Wood T (2013) A component-based performance comparison of four hypervisors. In: International Symposium on Integrated Network Management (IM), Ghent, Belgium, pp 269–276IBM (2006) An architectural blueprint for autonomic computing. White paper, 4th edIosup A, Ostermann S, Yigitbasi N, Prodan R, Fahringer T, Epema DHJ (2011) Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans Parallel Distrib Syst 22(6):931–945Ivanovic D, Carro M, Hermenegildo MV (2013) A sharing-based approach to supporting adaptation in service compositions. Computing 95(6):453–492Jiang Y, Perng C, Li T, Chang RN (2011) ASAP: A self-adaptive prediction system for instant cloud resource demand provisioning. In: 11th International Conference on Data Mining (ICDM), Vancouver, Canada, pp 1104–1109Johnson PR, Thomas RH (1975) The maintenance of duplicate databases. RFC 677, Network Working Group, Internet Engineering Task ForceKephart JO, Chess DM (2003) The vision of autonomic computing. IEEE Comput 36(1):41–50Kiviti A, Laor D, Costa G, Enberg P, Har’El N, Marti D, Zolotarov V (2014) OSv—Optimizing the operating system for virtual machines. In: USENIX Annual Technical Conference (ATC), Philadelphia, PA, USA, pp 61–72Knauth T, Fetzer C (2011) Scaling non-elastic applications using virtual machines. In: IEEE International Conference on Cloud Computing (CLOUD), Washington, DC, USA, pp 468–475Knauth T, Fetzer C (2014) DreamServer: truly on-demand cloud services. In: International Conference on Systems and Storage (SYSTOR), Haifa, Israel, pp 1–11Kramer J, Magee J (1990) The evolving philosophers problem: dynamic change management. IEEE Trans Softw Eng 16(11):1293–1306Lakshman A, Malik P (2010) Cassandra: a decentralized structured storage system. Oper Syst Rev 44(2):35–40Lang W, Shankar S, Patel JM, Kalhan A (2014) Towards multi-tenant performance SLOs. IEEE Trans Knowl Data Eng 26(6):1447–1463Langner F, Andrzejak A (2013) Detecting software aging in a cloud computing framework by comparing development versions. In: IFIP/IEEE International Symposium on Integrated Network Management (IM), Ghent, Belgium, pp 896–899Lazowska ED, Zahorjan J, Graham GS, Sevcik KC (1984) Quantitative system performance. Computer system analysis using queueing network models. Prentice Hall, Upper Saddle RiverLeitner P, Michlmayr A, Rosenberg F, Dustdar S (2010) Monitoring, prediction and prevention of SLA violations in composite services. In: IEEE International Conference on Web Services (ICWS), Florida, USA, Miami, pp 369–376Li W (2011) Evaluating the impacts of dynamic reconfiguration on the QoS of running systems. J Syst Softw 84(12):2123–2138Lim HC, Babu S, Chase JS, Parekh SS (2009) Automated control in cloud computing: challenges and opportunities. In: 1st ACM Workshop Automated Control Datacenters Clouds (ACDC), Barcelona, Spain, pp 13–18Liu J, Zhou J, Buyya R (2015) Software rejuvenation based fault tolerance scheme for cloud applications. In: 8th IEEE International Conference on Cloud Computing (CLOUD), New York City, NY, USA, pp 1115–1118Lorido-Botran T, Miguel-Alonso J, Lozano JA (2014) A review of auto-scaling techniques for elastic applications in cloud environments. J Grid Comput 12(4):559–592Massie M, Li B, Nicholes B, Vuksan V, Alexander R, Buchbinder J, Costa F, Dean A, Josephsen D, Phaal P, Pocock D (2012) Monitoring with Ganglia. O’Reilly Media, Tracking Dynamic Host and Application Metrics at Scale. ISBN 978-1-4493-2970-9Matias R Jr, Andrzejak A, Machida F, Elias D, Trivedi KS (2014) A systematic differential analysis for fast and robust detection of software aging. In: 33rd IEEE Symposium on Reliable Distributed Systems (SRDS). Nara, Japan, pp 311–320Medina V, García JM (2014) A survey of migration mechanisms of virtual machines. ACM Comput Surv 46(3):30Mell P, Grance T (2011) The NIST definition of cloud computing. Recommendations of the National Institute of Standards and Technology, Special Publication 800-145Menascé DA, Bennani MN (2006) Autonomic virtualized environments. In: International Conference on Autonomic and Autonomous Systems (ICAS), Silicon Valley, California, USA, p 28Menascé DA, Ngo P (2009) Understanding cloud computing: Experimentation and capacity planning. In: 35th International Computer Measurement Group Conference, Dallas, TX, USAMenascé DA, Ruan H, Gomaa H (2007) QoS management in service-oriented architectures. Perform Eval 64(7–8):646–663Miedes E, Muñoz-Escoí FD (2010) Dynamic switching of total-order broadcast protocols. In: International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), Las Vegas, Nevada, USA, pp 457–463Mohamed M (2014) Generic monitoring and reconfiguration for service-based applications in the cloud. PhD thesis, Université d’Evry-Val d’Essonne, FranceMohamed M, Amziani M, Belaïd D, Tata S, Melliti T (2015) An autonomic approach to manage elasticity of business processes in the cloud. Future Gener Comp Sys 50(C):49–61Mohd Yusoh ZI (2013) Composite SaaS resource management in cloud computing using evolutionary computation. PhD thesis, Sc Eng Faculty, Queensland University of Technology, Brisbane, AustraliaMontero RS, Moreno-Vozmediano R, Llorente IM (2011) An elasticity model for high throughput computing clusters. J Parallel Distrib Comput 71(6):750–757Morabito R, Kjällman J, Komu M (2015) Hypervisors vs. lightweight virtualization: a performance comparison. In: IEEE International Conference on Cloud Engineering (IC2E), Tempe, AZ, USA, pp 386–393Najjar A, Serpaggi X, Gravier C, Boissier O (2014) Survey of elasticity management solutions in cloud computing. In: Mahmood Z (ed) Continued rise of the cloud: advances and trends in cloud computing. Springer, Berlin, pp 235–263Naskos A, Gounaris A, Sioutas S (2015) Cloud elasticity: a survey. In: 1st International Workshop on Algorithmic Aspects of Cloud Computing (ALGOCLOUD), Patras, Greece, pp 151–167Neamtiu I, Dumitras T (2011) Cloud software upgrades: challenges and opportunities. In: IEEE International Workshop on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA), Williamsburg, VA, USA, pp 1–10Neuman BC (1994) Scale in distributed systems. In: Singhal M, Casavant TL (eds) Readings in Distributed computing systems. IEEE-CS Press, Los Alamitos, pp 463–489Padala P, Shin KG, Zhu X, Uysal M, Wang Z, Singhal S, Merchant A, Salem K (2007) Adaptive control of virtualized resources in utility computing environments. In: EuroSys Conference Lisbon, Portugal, pp 289–302Parnas DL (1994) Software aging. In: 6th International Conference on Software Engineering (ICSE), Sorrento, Italy, pp 279–287Parzen E (1960) A survey on time series analysis. Tech. rep., n. 37, Applied Mathematics and Statistics Laboratory, Stanford University, Stanford, CA, USAPascual-Miret L, González de Mendívil JR, Bernabéu-Aubán JM, Muñoz-Escoí FD (2015) Widening CAP consistency. Tech. rep., IUMTI-SIDI-2015/003, Univ. Politècnica de València, Valencia, SpainPopek GJ, Goldberg RP (1974) Formal requirements for virtualizable third generation architectures. Commun ACM 17(7):412–421Potter S, Nieh J (2005) AutoPod: Unscheduled system updates with zero data loss. In: 2nd International Conference on Autonomic Computing (ICAC), Seattle, WA, USA, pp 367–368Rajagopalan S (2014) System support for elasticity and high availability. PhD thesis, The University of British Columbia, Vancouver, CanadaReinecke P, Wolter K, van Moorsel APA (2010) Evaluating the adaptivity of computing systems. Perform Eval 67(8):676–693Rolia JA, Sevcik KC (1995) The method of layers. IEEE Trans Softw Eng 21(8):689–700Roy N, Dubey A, Gokhale AS (2011) Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 4th IEEE International Conference on Cloud Computing (CLOUD), Washington, DC, USA, pp 500–507Ruiz-Fuertes MI, Muñoz-Escoí FD (2009) Performance evaluation of a metaprotocol for database replication adaptability. In: 28th IEEE Symposium on Reliable Distributed Systems (SRDS), Niagara Falls, New York, USA, pp 32–38Saito Y, Shapiro M (2005) Optimistic replication. ACM Comput Surv 37(1):42–81Seifzadeh H, Abolhassani H, Moshkenani MS (2013) A survey of dynamic software updating. J Softw Evol Process 25(5):535–568Sharma U, Shenoy PJ, Sahu S, Shaikh A (2011) A cost-aware elasticity provisioning system for the cloud. In: International Conference on Distributed Computing Systems (ICDCS), Minneapolis, Minnesota, USA, pp 559–570Shen M, Kshemkalyani AD, Hsu TY (2015) Causal consistency for geo-replicated cloud storage under partial replication. In: International Parallel and Distributed Processing Symposium (IPDPS) Workshop, Hyderabad, India, pp 509–518Shen Z, Subbiah S, Gu X, Wilkes J (2011) CloudScale: elastic resource scaling for multi-tenant cloud systems. In: ACM Symposium on Cloud Computing (SOCC), Cascais, Portugal, p 5Simoes R, Kamienski CA (2014) Elasticity management in private and hybrid clouds. In: 7th IEEE International Conference on Cloud Computing (CLOUD), Anchorage, AK, USA, pp 793–800Singh S, Chana I (2015) QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput Surv 48(3):42:1–42:46Smith CU (1980) The prediction and evaluation of the performance of software from extended design specifications. PhD thesis, Department of Computer Science, The University of Texas at Austin, USASmith CU, Williams LG (2003) Software performance engineering. In: Lavagno L, Martin G, Selic B (eds) UML for real. Design of embedded real-time systems, chap 16. Springer, Berlin, pp 343–365Solarski M (2004) Dynamic upgrade of distributed software components. PhD thesis, Fakultät IV Elektronik und Informatik, Technischen Universität Berlin, Berlin, GermanySoltesz S, Pötzl H, Fiuczynski ME, Bavier AC, Peterson LL (2007) Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In: European Conference, Lisbon, Portugal, pp 275–287Soules CAN, Appavoo J, Hui K, Wisniewski RW, Silva DD, Ganger GR, Krieger O, Stumm M, Auslander MA, Ostrowski M, Rosenburg BS, Xenidis J (2003) System support for online reconfiguration. In: USENIX Annual Technical Conference. San Antonio, Texas, USA, pp 141–154Sridharan S (2012) A performance comparison of hypervisors for cloud computing. Master Thesis (paper 269), School of Computing, University of North Florida, USAStonebraker M (1986) The case for shared nothing. IEEE Database Eng Bull 9(1):4–9Sun D, Guimarans D, Fekete A, Gramoli V, Zhu L (2015) Multi-objective optimisation of rolling upgrade allowing for failures in clouds. In: 34th IEEE Symposium on Reliable Distributed Systems (SRDS). Montreal, QC, Canada, pp 68–73Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. The MIT Press, CambridgeToosi AN, Calheiros RN, Buyya R (2014) Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput Surv 47(1):7:1–7:47Vaquero González LM, Rodero-Merino L, Cáceres J, Lindner MA (2009) A break in the clouds: towards a cloud definition. Comput Commun Rev 39(1):50–55Varrette S, Guzek M, Plugaru V, Besseron X, Bouvry P (2013) HPC performance and energy-efficiency of Xen, KVM and VMware hypervisors. In: 25th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). Porto de Galinhas, Pernambuco, Brazil, pp 89–96Vasic N, Novakovic DM, Miucin S, Kostic D, Bianchini R (2012) DejaVu: accelerating resource allocation in virtualized environments. In: 17th nternational Conference on Architectural Support for Programing Languages and Operating Systems (ASPLOS), London, UK, pp 423–436Vaughan-Nichols SJ (2006) New approach to virtualization is a lightweight. IEEE Comput 39(11):12–14Vogels W (2009) Eventually consistent. Commun ACM 52(1):40–44Wada H, Suzuki J, Yamano Y, Oba K (2011) Evolutionary deployment optimization for service-oriented clouds. Softw Pract Exp 41(5):469–493Whitaker A, Cox RS, Shaw M, Gribble SD (2005) Rethinking the design of virtual machine monitors. IEEE Comput 38(5):57–62Wishart DMG (1969) A survey of control theory. J R Stat Soc Ser A-G 132(3):293–319Yataghene L, Amziani M, Ioualalen M, Tata S (2014) A queuing model for business processes elasticity evaluation. In: International Workshop on Advanced Information Systems for Enterprises (IWAISE), Tunis, Tunisia, pp 22–28Zawirski M, Preguiça N, Duarte S, Bieniusa A, Balegas V, Shapiro M (2015) Write fast, read in th

Plataformes avançades en el Núvol per a la reproductibilitat d'experiments computacionals

Tesis por compendio[ES] La tesis presentada se enmarca dentro del ámbito de la ciencia computacional. Dentro de esta, se centra en el desarrollo de herramientas para la ejecución de experimentación científica computacional, el impacto de la cual es cada vez mayor en todos los ámbitos de la ciencia y la ingeniería. Debido a la creciente complejidad de los cálculos realizados, cada vez es necesario un mayor conocimiento de las técnicas y herramientas disponibles para llevar a cabo este tipo de experimentos, ya que pueden requerir, en general, una gran infraestructura computacional para afrontar los altos costes de cómputo. Más aún, la reciente popularización del cómputo en la Nube ofrece una gran variedad de posibilidades para configurar nuestras propias infraestructuras con requisitos específicos. No obstante, el precio a pagar es la complejidad de configurar dichas infraestructuras en este tipo de entornos. Además, el aumento en la complejidad de configuración de los entornos en la nube no hace más que agravar un problema ya existente en el ámbito científico, y es el de la reproducibilidad de los resultados publicados. La falta de documentación, como las versiones de software que se han usado para llevar a cabo el cómputo, o los datos requeridos, provocan que una parte significativa de los resultados de experimentos computacionales publicados no sean reproducibles por otros investigadores. Como consecuencia, se produce un derroche de recursos destinados a la investigación. Como respuesta a esta situación, existen, y continúan desarrollándose, diferentes herramientas para facilitar procesos como el despliegue y configuración de infraestructura, el acceso a los datos, el diseño de flujos de cómputo, etc. con el objetivo de que los investigadores puedan centrarse en el problema a abordar. Precisamente, esta es la base de los trabajos desarrollados en la presente tesis, el desarrollo de herramientas para facilitar que el cómputo científico se beneficie de entornos de computación en la Nube de forma eficiente. El primer trabajo presentado empieza con un estudio exhaustivo de las prestaciones d'un servicio relativamente nuevo, la ejecución serverless de funciones. En este, se determinará la conveniencia de usar este tipo de entornos en el cálculo científico midiendo tanto sus prestaciones de forma aislada, como velocidad de CPU y comunicaciones, como en conjunto mediante el desarrollo de una aplicación de procesamiento MapReduce para entornos serverless. En el siguiente trabajo, se abordará una problemática diferente, y es la reproducibilidad de experimentos computacionales. Para conseguirlo, se presentará un entorno, basado en Jupyter, donde se encapsule tanto el proceso de despliegue y configuración de infraestructura computacional como el acceso a datos y la documentación de la experimentación. Toda esta información quedará registrada en el notebook de Jupyter donde se ejecuta el experimento, permitiendo así a otros investigadores reproducir los resultados simplemente compartiendo el notebook correspondiente. Volviendo al estudio de las prestaciones del primer trabajo, teniendo en cuenta las medidas y bien estudiadas fluctuaciones de éstas en entornos compartidos, como el cómputo en la Nube, en el tercer trabajo se desarrollará un sistema de balanceo de carga diseñado expresamente para este tipo de entornos. Como se mostrará, este componente es capaz de gestionar y corregir de forma precisa fluctuaciones impredecibles en las prestaciones del cómputo en entornos compartidos. Finalmente, y aprovechando el desarrollo anterior, se diseñará una plataforma completamente serverless encargada de repartir y balancear tareas ejecutadas en múltiples infraestructuras independientes. La motivación de este último trabajo viene dada por los altos costes computacionales de ciertos experimentos, los cuales fuerzan a los investigadores a usar múltiples infraestructuras que, en general, pertenecen a diferentes organizaciones.[CA] La tesi presentada a aquest document s'emmarca dins de l'àmbit de la ciència computacional. Dintre d'aquesta, es centra en el desenvolupament d'eines per a l'execució d'experimentació científica computacional, la qual té un impacte cada vegada major en tots els àmbits de la ciència i l'enginyeria. Donada la creixent complexitat dels càlculs realitzats, cada vegada és necessari un major coneixement sobre les tècniques i eines disponibles per a dur a terme aquestes experimentacions, ja que poden requerir, en general, una gran infraestructura computacional per afrontar els alts costos de còmput. Més encara, la recent popularització del còmput en el Núvol ofereix una gran varietat de possibilitats per a configurar les nostres pròpies infraestructures amb requisits específiques. No obstant, el preu a pagar és la complexitat de configurar les esmenades infraestructures a aquest tipus d'entorns. A més, l'augment de la complexitat de configuració dels entorns de còmput no ha fet més que agreujar un problema ja existent a l'àmbit científic, i és la reproductibilitat de resultats publicats. La manca de documentació, com les versions del programari emprat per a dur a terme el còmput, o les dades requerides ocasionen que una part no negligible dels resultats d'experiments computacionals publicats no siguen reproduïbles per altres investigadors. Com a conseqüència, es produeix un malbaratament dels recursos destinats a la investigació. Com a resposta a aquesta situació, existeixen, i continuen desenvolupant-se, diverses eines per facilitar processos com el desplegament i configuració d'infraestructura, l'accés a les dades, el disseny de fluxos de còmput, etc. amb l'objectiu de que els investigadors puguen centrar-se en el problema a abordar. Precisament, aquesta és la base dels treballs desenvolupats durant la tesi que segueix, el desenvolupar eines per a facilitar que el còmput científic es beneficiar-se d'entorns de computació en el Núvol d'una forma eficient. El primer treball presentat comença amb un estudi exhaustiu de les prestacions d'un servei relativament nou, l'execució serverless de funcions. En aquest, es determinarà la conveniència d'emprar este tipus d'entorns en el càlcul científic mesurant tant les seues prestacions de forma aïllada, com velocitat de CPU i la velocitat de les comunicacions, com en conjunt a través del desenvolupament d'una aplicació de processament MapReduce per a entorns serverless. Al següent treball, s'abordarà una problemàtica diferent, i és la reproductibilitat dels experiments computacionals. Per a aconseguir-ho, es presentarà una entorn, basat en Jupyter, on s'englobe tant el desplegament i configuració d'infraestructura computacional, com l'accés a les dades requerides i la documentació de l'experimentació. Tota aquesta informació quedarà registrada al notebook de Jupyter on s'executa l'experiment, permetent així a altres investigadors reproduir els resultats simplement compartint el notebook corresponent. Tornant a l'estudi de les prestacions del primer treball, donades les mesurades i ben estudiades fluctuacions d'aquestes en entorns compartits, com en el còmput en el Núvol, al tercer treball es desenvoluparà un sistema de balanceig de càrrega dissenyat expressament per aquest tipus d'entorns. Com es veurà, aquest component és capaç de gestionar i corregir de forma precisa fluctuacions impredictibles en les prestacions de còmput d'entorns compartits. Finalment, i aprofitant el desenvolupament anterior, es dissenyarà una plataforma completament serverless per a repartir i balancejar tasques executades en múltiples infraestructures de còmput independents. La motivació d'aquest últim treball ve donada pels alts costos computacionals de certes experimentacions, els quals forcen als investigadors a emprar múltiples infraestructures que, en general, pertanyen a diferents organitzacions. Es demostrarà la capacitat de la plataforma per balancejar treballs i minimitzar el malbaratament de recursos[EN] This document is focused on computational science, specifically in the development of tools for executions of scientific computational experiments, whose impact has increased, and still increasing, in all scientific and engineering scopes. Considering the growing complexity of scientific calculus, it is required large and complex computational infrastructures to carry on the experimentation. However, to use this infrastructures, it is required a deep knowledge of the available tools and techniques to be handled efficiently. Moreover, the popularity of Cloud computing environments offers a wide variety of possible configurations for our computational infrastructures, thus complicating the configuration process. Furthermore, this increase in complexity has exacerbated the well known problem of reproducibility in science. The lack of documentation, as the used software versions, or the data required by the experiment, produces non reproducible results in computational experiments. This situation produce a non negligible waste of the resources invested in research. As consequence, several tools have been developed to facilitate the deployment, usage and configuration of complex infrastructures, provide access to data, etc. with the objective to simplify the common steps of computational experiments to researchers. Moreover, the works presented in this document share the same objective, i.e. develop tools to provide an easy, efficient and reproducible usage of cloud computing environments for scientific experimentation. The first presented work begins with an exhaustive study of the suitability of the AWS serverless environment for scientific calculus. In this one, the suitability of this kind of environments for scientific research will be studied. With this aim, the study will measure the CPU and network performance, both isolated and combined, via a MapReduce framework developed completely using serverless services. The second one is focused on the reproducibility problem in computational experiments. To improve reproducibility, the work presents an environment, based on Jupyter, which handles and simplify the deployment, configuration and usage of complex computational infrastructures. Also, includes a straight forward procedure to provide access to data and documentation of the experimentation via the Jupyter notebooks. Therefore, the whole experiment could be reproduced sharing the corresponding notebook. In the third work, a load balance library has been developed to address fluctuations of shared infrastructure capabilities. This effect has been wide studied in the literature and affects specially to cloud computing environments. The developed load balance system, as we will see, can handle and correct accurately unpredictable fluctuations in such environments. Finally, based on the previous work, a completely serverless platform is presented to split and balance job executions among several shared, heterogeneous and independent computing infrastructures. The motivation of this last work is the huge computational cost of many experiments, which forces the researchers to use multiple infrastructures belonging, in general, to different organisations. It will be shown how the developed platform is capable to balance the workload accurately. Moreover, it can fit execution time constrains specified by the user. In addition, the platform assists the computational infrastructures to scale as a function of the incoming workload, avoiding an over-provisioning or under-provisioning. Therefore, the platform provides an efficient usage of the available resources.This study was supported by the program “Ayudas para la contratación de personal investigador en formación de carácter predoctoral, programa VALi+d” under grant number ACIF/2018/148 from the Conselleria d’Educació of the Generalitat Valenciana. The authors would also like to thank the Spanish "Ministerio de Economía, Industria y Competitividad"for the project “BigCLOE” with reference number TIN2016-79951-R.Giménez Alventosa, V. (2022). Plataformes avançades en el Núvol per a la reproductibilitat d'experiments computacionals [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/184010TESISCompendi