Grid Computing in Extreme Situations: Reducing Risk and Creating Resilience for IT-Infrastructures

Recent turbulences in financial markets are not only a challenge for the actors in the front offices of the related institutions, but also represent a serious challenge for the IT departments in the back offices of banks etc. We present a simulation model that shows how Grid computing increases the resilience and quality-of-service of IT infrastructure in departmentalized enterprises in the presence of shocks. Grid computing also reduces the costs deriving from the cancellation of jobs in times with a high volatility of computational load. The model can be used to find the appropriate type of IT infrastructure for different financial service institutions. Our simulations\u27 findings are also likely to encourage the introduction of Grid computing for related business branches and applications

A service-oriented architecture for scientific computing on cloud infrastructures

This paper describes a service-oriented architecture that eases the process of scientific application deployment and execution in IaaS Clouds, with a focus on High Throughput Computing applications. The system integrates i) a catalogue and repository of Virtual Machine Images, ii) an application deployment and configuration tool, iii) a meta-scheduler for job execution management and monitoring. The developed system significantly reduces the time required to port a scientific application to these computational environments. This is exemplified by a case study with a computationally intensive protein design application on both a private Cloud and a hybrid three-level infrastructure (Grid, private and public Cloud).The authors wish to thank the financial support received from the Generalitat Valenciana for the project GV/2012/076 and to the Ministerio de Econom´ıa y Competitividad for the project CodeCloud (TIN2010-17804)Moltó, G.; Calatrava Arroyo, A.; Hernández García, V. (2013). A service-oriented architecture for scientific computing on cloud infrastructures. En High Performance Computing for Computational Science - VECPAR 2012. Springer Verlag (Germany). 163-176. doi:10.1007/978-3-642-38718-0_18S163176Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds. ACM SIGCOMM Computer Communication Review 39(1), 50 (2008)Armbrust, M., Fox, A., Griffith, R., Joseph, A.: Above the clouds: A berkeley view of cloud computing. Technical report, UC Berkeley Reliable Adaptive Distributed Systems Laboratory (2009)Rehr, J., Vila, F., Gardner, J., Svec, L., Prange, M.: Scientific computing in the cloud. Computing in Science 99 (2010)Keahey, K., Figueiredo, R., Fortes, J., Freeman, T., Tsugawa, M.: Science Clouds: Early Experiences in Cloud Computing for Scientific Applications. In: Cloud Computing and its Applications (2008)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)Moltó, G., Hernández, V., Alonso, J.: A service-oriented WSRF-based architecture for metascheduling on computational Grids. Future Generation Computer Systems 24(4), 317–328 (2008)Krishnan, S., Clementi, L., Ren, J., Papadopoulos, P., Li, W.: Design and Evaluation of Opal2: A Toolkit for Scientific Software as a Service. In: 2009 IEEE Congress on Services (2009)Distributed Management Task Force (DMTF): The Open Virtualization Format Specification (Technical report)Raman, R., Livny, M., Solomon, M.: Matchmaking: Distributed Resource Management for High Throughput Computing. In: Proceedings of the Seventh IEEE International Symposium on High Performance Distributed Computing, pp. 28–31 (1998)Wei, J., Zhang, X., Ammons, G., Bala, V., Ning, P.: Managing security of virtual machine images in a cloud environment. ACM Press, New York (2009)Keahey, K., Freeman, T.: Contextualization: Providing One-Click Virtual Clusters. In: Fourth IEEE International Conference on eScience, pp. 301–308 (2008)Foster, I.: Globus toolkit version 4: Software for service-oriented systems. Journal of Computer Science and Technology 21(4), 513–520 (2006)Moltó, G., Suárez, M., Tortosa, P., Alonso, J.M., Hernández, V., Jaramillo, A.: Protein design based on parallel dimensional reduction. Journal of Chemical Information and Modeling 49(5), 1261–1271 (2009)Calatrava, A.: In: Use of Grid and Cloud Hybrid Infrastructures for Scientific Computing (M.Sc. Thesis in Spanish), Universitat Politècnica de València (2012)Keahey, K., Freeman, T., Lauret, J., Olson, D.: Virtual workspaces for scientific applications. Journal of Physics: Conference Series 78(1), 012038 (2007)Pallickara, S., Pierce, M., Dong, Q., Kong, C.: Enabling Large Scale Scientific Computations for Expressed Sequence Tag Sequencing over Grid and Cloud Computing Clusters. In: Eigth International Conference on Parallel Processing and Applied Mathematics (PPAM 2009), Citeseer (2009)Merzky, A., Stamou, K., Jha, S.: Application Level Interoperability between Clouds and Grids. In: 2009 Workshops at the Grid and Pervasive Computing Conference, pp. 143–150 (2009)Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: the Condor experience. Concurrency and Computation: Practice and Experience 17(2-4), 323–356 (2005)Simmhan, Y., van Ingen, C., Subramanian, G., Li, J.: Bridging the Gap between Desktop and the Cloud for eScience Applications. In: 2010 IEEE 3rd International Conference on Cloud Computing, pp. 474–481. IEEE (2010)Chappell, D.: Introducing windows azure. Technical report (2009

Integrating BOINC with Microsoft Excel: A case study

The convergence of conventional Grid computing with public resource computing (PRC) offers potential benefits in the enterprise setting. For this work we took the popular PRC toolkit BOINC and used it to execute a previously monolithic Microsoft Excel financial model across several commodity computers. Our experience indicates that speedup approaching linear may be realised for certain scenarios, and that this approach offers a viable route to leveraging idle desktop PCs in the enterprise

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. 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Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

Personal Volunteer Computing

We propose personal volunteer computing, a novel paradigm to encourage technical solutions that leverage personal devices, such as smartphones and laptops, for personal applications that require significant computations, such as animation rendering and image processing. The paradigm requires no investment in additional hardware, relying instead on devices that are already owned by users and their community, and favours simple tools that can be implemented part-time by a single developer. We show that samples of personal devices of today are competitive with a top-of-the-line laptop from two years ago. We also propose new directions to extend the paradigm

Leveraging simulation practice in industry through use of desktop grid middleware

This chapter focuses on the collaborative use of computing resources to support decision making in industry. Through the use of middleware for desktop grid computing, the idle CPU cycles available on existing computing resources can be harvested and used for speeding-up the execution of applications that have “non-trivial” processing requirements. This chapter focuses on the desktop grid middleware BOINC and Condor, and discusses the integration of commercial simulation software together with free-to-download grid middleware so as to offer competitive advantage to organizations that opt for this technology. It is expected that the low-intervention integration approach presented in this chapter (meaning no changes to source code required) will appeal to both simulation practitioners (as simulations can be executed faster, which in turn would mean that more replications and optimization is possible in the same amount of time) and the management (as it can potentially increase the return on investment on existing resources)