CloudEx: a novel cloud-based task execution framework

In recent years cloud computing has seen steady adoption due to its unique features such as computing resource elasticity, fault-tolerance and utility billing. Cloud computing Infrastructure-as-a-Service (IaaS) enables unique architectures that can dynamically scale and configure computing resources from a catalogue of available features. In addition to provisioning long running homogeneous clusters of Virtual Machines (VMs), it can also be feasible to provision ephemeral and heterogeneous per-job VMs. This is made possible due to the reduced VM startup time and per- minute billing for cloud VMs. In this paper we design and implement CloudEx, a generic and novel framework for executing jobs on public clouds by leveraging the Google Cloud Platform. CloudEx enables users to split jobs into a sequence of smaller tasks that can be distributed using Bin Packing or user-defined algorithm. Additionally, users can specify the VM specification per job or per task, CloudEx then provisions the required VMs, coordinates the job execution and terminates these VMs once the job is completed

A novel cloud based elastic framework for big data preprocessing

A number of analytical big data services based on the cloud computing paradigm such as Amazon Redshift and Google Bigquery have recently emerged. These services are based on columnar databases rather than traditional Relational Database Management Systems (RDBMS) and are able to analyse massive datasets in mere seconds. This has led many organisations to retain and analyse their massive logs, sensory or marketing datasets, which were previously discarded due to the inability to either store or analyse them. Although these big data services have addressed the issue of big data analysis, the ability to efficiently de-normalise and prepare this data to a format that can be imported into these services remains a challenge. This paper describes and implements a novel, generic and scalable cloud based elastic framework for Big Data Preprocessing (BDP). Since the approach described by this paper is entirely based on cloud computing it is also possible to measure the overall cost incurred by these preprocessing activities

A platform to deploy customized scientific virtual infrastructures on the cloud

This paper presents a software platform to dynamically deploy complex scientific virtual computing infrastructures, on top of Infrastructure as a Service (IaaS) Clouds. The platform orchestrates different services to provision the virtual computing resources. It dynamically installs the appropriate software to satisfy the requirements of a researcher, both on public and on-premise Clouds. The platform provides a web interface to enable the users to easily management of the lifecycle of virtual infrastructures. It enables users to define infrastructures, share them with other users, deploy and relinquish them, add or remove resources dynamically, create and share application recipes, etc. The paper also describes three case studies to deploy complex infrastructures, namely a Hadoop cluster, a single-node to perform NGS sequencing and a gateway for users to access the European Grid Infrastructure (EGI). This platform promotes a better use of on-premise hardware resources of a research center by allocating the computing resources just-in-time to the specific life time of the virtual infrastructures as well as the deployment of the very same infrastructures on a public Cloud.The authors would to thank the Spanish "Ministerio de Economia y Competitividad" for the project "Clusters Virtuales Elasticos y Migrables sobre Infraestructuras Cloud Hibridas" with reference TIN2013-44390-R.Caballer Fernández, M.; Segrelles Quilis, JD.; Moltó, G.; Blanquer Espert, I. (2015). A platform to deploy customized scientific virtual infrastructures on the cloud. Concurrency and Computation: Practice and Experience. 27(16):4318-4329. https://doi.org/10.1002/cpe.3518S431843292716Mell P Grance T The NIST definition of Cloud computing. NIST Special Publication 800-145 (Final) Technical Report 2011 http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdfBuyya, R., Broberg, J., & Goscinski, A. (Eds.). (2011). Cloud Computing. doi:10.1002/9780470940105Sahoo J Mohapatra S Lath R Virtualization: a survey on concepts, taxonomy and associated security issues 2010 Second International Conference on Computer and Network Technology Bangkok, Thailand 2010 222 226OpenStack OpenStack 2013 http://openstack.orgNurmi D Wolski R Grzegorczyk C Obertelli G Soman S Youseff L Zagorodnov D The Eucalyptus open-source Cloud-computing system Proceedings of 9th IEEE International Symposium on Cluster Computing and the Grid Shanghai, China 2009 124 131Amazon Web Services AWS CloudFormation http://aws.amazon.com/cloudformation/Amazon Web Services AWS OpsWorks http://aws.amazon.com/opsworks/Keahey K Freeman T Contextualization: providing one-click virtual clusters Fourth IEEE International Conference on eScience Indianapolis, Indiana, USA 2008 301 308Keahey K Freeman T Architecting a large-scale elastic environment: recontextualization and adaptive Cloud services for scientific computing 2012Marshall P Keahey K Freeman T Elastic site: using Clouds to elastically extend site resources Proceedings of the 2010 IEEE/ACM 10th International Conference on Cluster, Cloud and Grid Computing CCGRID '10 IEEE Computer Society, Washington, DC, USA 2010 43 52Bresnahan J Freeman T LaBissoniere D Keahey K Managing appliance launches in infrastructure Clouds Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery TG '11 ACM, New York, NY, USA 2011 12:1 12:7Apache Whirr 2013 from:http://whirr.apache.org/Juve G Deelman E Automating application deployment in infrastructure clouds Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science CLOUDCOM '11 IEEE Computer Society, Washington, DC, USA 2011 658 665OASIS Topology and orchestration specification for cloud applications version 1.0 2013 http://docs.oasis-open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.htmlBinz T Breitenbcher U Haupt F Kopp O Leymann F Nowak A Wagner S OpenTOSCA - a runtime for TOSCA-based cloud applications ICSOC, Lecture Notes in Computer Science 8274 Springer 2013 692 695Puppet Labs IT automation software for system administrators 2013 http://www.puppetlabs.com/Opscode Chef 2013 http://www.opscode.com/chef/DeHaan M Ansible 2013 http://ansible.cc/Vogels, W. (2008). Beyond server consolidation. Queue, 6(1), 20. doi:10.1145/1348583.1348590Carrión JV Moltó G De Alfonso C Caballer M Hernández V A generic catalog and repository service for virtual machine images 2nd International ICST Conference on Cloud Computing (CloudComp 2010) Barcelona, Spain 2010 1 15de Alfonso C Caballer M Alvarruiz F Molto G Hernández V Infrastructure deployment over the Cloud 2011 IEEE Third International Conference on Cloud Computing Technology and Science Athens, Greece 2011 517 521Caballer, 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/s10723-014-9296-5Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107. doi:10.1145/1327452.1327492Shvachko K Kuang H Radia S Chansler R The Hadoop distributed file system 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) Incline Village, NV, USA 2010 1 10Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410. doi:10.1016/s0022-2836(05)80360-

Dynamic management of virtual infrastructures

The final publication is available at Springer via http://dx.doi.org/10.1007/s10723-014-9296-5Cloud infrastructures are becoming an appropriate solution to address the computational needs of scientific applications. However, the use of public or on-premises Infrastructure as a Service (IaaS) clouds requires users to have non-trivial system administration skills. Resource provisioning systems provide facilities to choose the most suitable Virtual Machine Images (VMI) and basic configuration of multiple instances and subnetworks. Other tasks such as the configuration of cluster services, computational frameworks or specific applications are not trivial on the cloud, and normally users have to manually select the VMI that best fits, including undesired additional services and software packages. This paper presents a set of components that ease the access and the usability of IaaS clouds by automating the VMI selection, deployment, configuration, software installation, monitoring and update of Virtual Appliances. It supports APIs from a large number of virtual platforms, making user applications cloud-agnostic. In addition it integrates a contextualization system to enable the installation and configuration of all the user required applications providing the user with a fully functional infrastructure. Therefore, golden VMIs and configuration recipes can be easily reused across different deployments. Moreover, the contextualization agent included in the framework supports horizontal (increase/decrease the number of resources) and vertical (increase/decrease resources within a running Virtual Machine) by properly reconfiguring the software installed, considering the configuration of the multiple resources running. This paves the way for automatic virtual infrastructure deployment, customization and elastic modification at runtime for IaaS clouds.The authors would like to thank to thank the financial support received from the Ministerio de Economia y Competitividad for the project CodeCloud (TIN2010-17804).Caballer Fernández, M.; Blanquer Espert, I.; Moltó, G.; Alfonso Laguna, CD. (2015). Dynamic management of virtual infrastructures. Journal of Grid Computing. 13(1):53-70. https://doi.org/10.1007/s10723-014-9296-5S5370131de Alfonso, C., Caballer, M., Alvarruiz, F., Molto, G., Hernández, V.: Infrastructure deployment over the cloud. In: 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, pp. 517–521. IEEE. (2011). doi: 10.1109/CloudCom.2011.77Alvarruiz, F., De Alfonso, C., Caballer, M., Hernández, V.: An energy manager for high performance computer clusters. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp. 231–238. (2012). doi: 10.1109/ISPA.2012.38Amazon Web Services: AWS CloudFormation. (2013). http://aws.amazon.com/es/cloudformation/Apache: Whirr (2013). http://whirr.apache.org/Blanquer, I., Brasche, G., Lezzi, D.: Requirements of scientific applications in cloud offerings. In: Proceedings of the 2012 6th Iberian Grid Infrastructure Conference, IBERGRID ’12, pp. 173–182 (2012)Bresnahan, J., Freeman, T., LaBissoniere, D., Keahey, K.: Managing appliance launches in infrastructure clouds. In: Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, TG ’11, pp. 12:1–12:7. ACM, New York (2011). doi: 10.1145/2016741.2016755Buyya, R., Ranjan, R., Calheiros, R.N.: InterCloud: utility-oriented federation of cloud computing environments for scaling of application services. Algoritm. Archit. Parallel Process. 6081, 20 (2010)Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009). doi: 10.1016/j.future.2008.12.001Caballer, M., De Alfonso, C., Alvarruiz, F., Moltó, G.: EC3: elastic cloud computing cluster. J. Comput. Syst. Sci. (2013). doi: 10.1016/j.jcss.2013.06.005Caballer, M., García, A., Moltó, G., de Alfonso, C.: Towards SLA-driven management of cloud infrastructures to elastically execute scientific applications. In: 6th Iberian Grid Infrastructure Conference (IberGrid), pp. 207–218 (2012)Carrión, J.V., Moltó, G., De Alfonso, C., Caballer, M., Hernández, V.: A generic catalog and repository service for virtual machine images. In: 2nd International ICST Conference on Cloud Computing CloudComp 2010 (2010)Cuomo, A., Modica, G., Distefano, S., Puliafito, A., Rak, M., Tomarchio, O., Venticinque, S., Villano, U.: An SLA-based broker for cloud infrastructures. J. Grid Comput 11(1), 1–25 (2012). doi: 10.1007/s10723-012-9241-4DeHaan, M.: Ansible. http://ansible.cc/ (2013)Distributed Management Task Force, Inc: Open Virtualization Format (OVF) (2010). http://dmtf.org/sites/default/files/standards/documents/DSP0243_1.1.0.pdfDistributed Management Task Force, Inc: Cloud Infrastructure Management Interface (CIMI) Model and REST Interface over HTTP Specification (2012). http://dmtf.org/sites/default/files/standards/documents/DSP0263_1.0.1.pdfEGI.eu: Seeking new horizons: EGI’s role for 2020. Tech. rep. (2012). https://documents.egi.eu/public/RetrieveFile?docid=1098&version=4&filename=EGI-1098-D230-final.pdfElmroth, E., Tordsson, J., Hernández, F.: Self-management challenges for multi-cloud architectures. Towards a service-based internet. Lect. Notes Comput. Sci. 6994, 38–49 (2011)HashiCorp: Vagrant (2013). http://www.vagrantup.com/Jacob, A.: Infrastructure in the cloud era. In: Proceedings of the 2009 International OReilly Conference Velocity (2009)Juve, G., Deelman, E.: Automating application deployment in infrastructure clouds. In: Proceedings of the 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, CLOUDCOM ’11, pp. 658–665. IEEE Computer Society, Washington DC (2011). doi: 10.1109/CloudCom.2011.102Keahey, K., Freeman, T.: Contextualization: providing one-click virtual clusters. In: 4th IEEE International Conference on eScience, pp. 301–308 (2008)Keahey, K., Freeman, T.: Architecting a large-scale elastic environment: recontextualization and adaptive cloud services for scientific computing (2012)Kecskemeti, G., Kertesz, A., Marosi, A., Kacsuk, P.: Interoperable resource management for establishing federated clouds. In: Achieving Federated and SelfManageable Cloud Infrastructures Theory and Practice, pp. 18–35 (2012). doi: 10.4018/978-1-4666-1631-8.ch002Kertesz, A., Kecskemeti, G., Oriol, M., Kotcauer, P., Acs, S., Rodríguez, M., Mercè, O., Marosi, A.C., Marco, J., Franch, X.: Enhancing federated cloud management with an integrated service monitoring approach. J. Grid Comput. 11(4), 699–720 (2013). doi: 10.1007/s10723-013-9269-0Loutas, N., Kamateri, E., Bosi, F., Tarabanis, K.: Cloud computing interoperability: the state of play. 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science, pp. 752–757 (2011). doi: 10.1109/CloudCom.2011.116Marshall, P., Keahey, K., Freeman, T.: Elastic site: using clouds to elastically extend site resources. In: Proceedings of the 2010 IEEE/ACM 10th International Conference on Cluster, Cloud and Grid Computing, CCGRID ’10, pp. 43–52. IEEE Computer Society, Washington DC (2010). doi: 10.1109/CCGRID.2010.80Massie, M.L., Chun, B.N., Culler, D.E.: The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput. 30(5-6), 817–840 (2004)Mell, P., Grance, T.: The NIST definition of cloud computing. NIST Special Publication 800-145 (Final). Tech. rep. (2011). http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdfMoltó, G., Caballer, M., Romero, E., Alfonso, C.D.: Elastic memory management of virtualized infrastructures for applications with dynamic memory requirements. In: Proceedings of the International Conference on Computational Science ICCS 2013, pp. 159–168. Elsevier (2013). doi: 10.1016/j.procs.2013.05.179Morfeo: Claudia (2013). http://claudia.morfeo-project.org/wiki/index.php/Main_PageOASIS: Topology and Orchestration Specification for Cloud Applications Version 1.0 (2013). http://docs.oasis-open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.htmlOCCI working group within the Open Grid Forum: Open Cloud Computing Interface Infrastructure (2011). http://ogf.org/documents/GFD.184.pdfOpscode: Chef (2013). http://www.opscode.com/chef/Pawluk, P., Simmons, B., Smit, M., Litoiu, M., Mankovski, S.: Introducing STRATOS: a cloud broker service. In: 2012 IEEE 5th International Conference on Cloud Computing, pp. 891–898 (2012). doi: 10.1109/CLOUD.2012.24Puppet Labs: IT Automation Software for System Administrators (2013). http://www.puppetlabs.com/Redl, C., Breskovic, I., Brandic, I., Dustdar, S.: Automatic SLA matching and provider selection in grid and cloud computing markets. In: Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing, GRID ’12, pp. 85–94. IEEE Computer Society, Washington (2012). doi: 10.1109/Grid.2012.18Rodero-Merino, L., Vaquero, L.M., Gil, V., Galán, F., Fontán, J., Montero, R.S., Llorente, I.M.: From infrastructure delivery to service management in clouds. Futur. Gener. Comput. Syst. 26(8), 1226–1240 (2010). doi: 10.1016/j.future.2010.02.013StratusLab: Claudia Platform (2013). http://stratuslab.eu/doku.php/claudiaSundareswaran, S., Squicciarini, A., Lin, D.: A brokerage-based approach for cloud service selection. In: Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing, CLOUD ’12, pp. 558–565 (2012). doi: 10.1109/CLOUD.2012.119Telefónica Investigación y Desarrollo S.A. Unipersonal.: Telefónicas TCloud API Specification. (2010). http://www.tid.es/files/doc/apis/TCloud_API_Spec_v0.9.pdfYangui, S., Marshall, I.J., Laisne, J.P., Tata, S.: CompatibleOne: The open source cloud broker. J. Grid Comput. (2013). doi: 10.1007/s10723-013-9285-

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

CodeCloud: A platform to enable execution of programming models on the Clouds

This paper presents a platform that supports the execution of scientific applications covering different programming models (such as Master/Slave, Parallel/MPI, MapReduce and Workflows) on Cloud infrastructures. The platform includes (i) a high-level declarative language to express the requirements of the applications featuring software customization at runtime, (ii) an approach based on virtual containers to encapsulate the logic of the different programming models, (iii) an infrastructure manager to interact with different IaaS backends, (iv) a configuration software to dynamically configure the provisioned resources and (v) a catalog and repository of virtual machine images. By using this platform, an application developer can adapt, deploy and execute parallel applications agnostic to the Cloud backend.The authors wish to thank the financial support received from both the Spanish Ministry of Economy and Competitiveness to develop the project "Servicios avanzados para el despliegue y contextualizacion de aplicaciones virtualizadas para dar soporte a modelos de programacion en entornos cloud", with reference TIN2010-17804.Caballer Fernández, M.; Alfonso Laguna, CD.; Moltó, G.; Romero Alcalde, E.; Blanquer Espert, I.; García García, A. (2014). CodeCloud: A platform to enable execution of programming models on the Clouds. Journal of Systems and Software. 93:187-198. https://doi.org/10.1016/j.jss.2014.02.005S1871989

Optimizing Computations and Allocations on High Performance and Cloud Computing Systems

Over the last decade, many research and development projects have focused on Cloud Computing systems. After forming around the early research papers and the first commercial cloud offerings in 2006-2008, the field has seen a tremendous progress and has provided the primary infrastructure and technology for applications at small, medium, and large scales. Cloud Computing systems have provided diverse on-demand resources to individual researchers and developers, groups and entire institutions, as well as commercial companies and government organizations. Clouds have also found their niche in scientific computing applications, offering attractive alternatives to High Performance Computing models and systems. While cloud economics and technologies have significantly matured recently, there is much active research revolving around topics such as optimality, usability, manageability, and reproducibility in the latest studies. This dissertation presents our findings and relevant developments at the intersection of Cloud Computing and such “flavors” of computing as High Performance Computing and High Throughput Computing. We primarily focus on optimality issues in this area and propose solutions that address the needs of individual researchers and research groups with limited computational and financial resources

Optimizing Computations and Allocations on High Performance and Cloud Computing Systems

Over the last decade, many research and development projects have focused on Cloud Computing systems. After forming around the early research papers and the first commercial cloud offerings in 2006-2008, the field has seen a tremendous progress and has provided the primary infrastructure and technology for applications at small, medium, and large scales. Cloud Computing systems have provided diverse on-demand resources to individual researchers and developers, groups and entire institutions, as well as commercial companies and government organizations. Clouds have also found their niche in scientific computing applications, offering attractive alternatives to High Performance Computing models and systems. While cloud economics and technologies have significantly matured recently, there is much active research revolving around topics such as optimality, usability, manageability, and reproducibility in the latest studies. This dissertation presents our findings and relevant developments at the intersection of Cloud Computing and such “flavors” of computing as High Performance Computing and High Throughput Computing. We primarily focus on optimality issues in this area and propose solutions that address the needs of individual researchers and research groups with limited computational and financial resources

Single system image: A survey

Single system image is a computing paradigm where a number of distributed computing resources are aggregated and presented via an interface that maintains the illusion of interaction with a single system. This approach encompasses decades of research using a broad variety of techniques at varying levels of abstraction, from custom hardware and distributed hypervisors to specialized operating system kernels and user-level tools. Existing classification schemes for SSI technologies are reviewed, and an updated classification scheme is proposed. A survey of implementation techniques is provided along with relevant examples. Notable deployments are examined and insights gained from hands-on experience are summarized. Issues affecting the adoption of kernel-level SSI are identified and discussed in the context of technology adoption literature

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903