100,715 research outputs found
A Parallel Implementation of the K Nearest Neighbours Classifier in Three Levels: Threads MPI Processes and the Grid
The work described in this paper tackles the problem of data mining and classification of large amounts of data using the K nearest neighbours classifier (KNN) [1]. The large computing demand of this process is solved with a parallel computing implementation specially designed to work in Grid environments of multiprocessor computer farms. The different parallel computing approaches (intra-node, inter-node and inter-organisations) are not sufficient by themselves to face the computing demand of such a big problem. Instead of using parallel techniques separately, we propose to combine the three of them considering the parallelism grain of the different parts of the problem. The main purpose is to complete a 1 month-CPU job in a few hours. The technologies that are being used are the EGEE Grid Computing Infrastructure running the Large Hadron Collider Computing Grid (LCG 2.6) middleware [3], MPI [4] [5] and POSIX [6] threads. Finally, we compare the results obtained with the most popular and used tools to understand the importance of this strategy.Aparicio Pla, G.; Blanquer Espert, I.; Hernández GarcĂa, V. (2007). A Parallel Implementation of the K Nearest Neighbours Classifier in Three Levels: Threads MPI Processes and the Grid. En High Performance Computing for Computational Science - VECPAR 2006. Springer Verlag (Germany). 225-235. doi:10.1007/978-3-540-71351-7_18S225235Cover, T.M., Hart, P.E.: Nearest neighbour pattern recognition. IEEE Trans. on Information Theory 13(1), 2127 (1967)Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications 15(3) (2001), http://www.globus.org/research/papers/anatomy.pdfLCG: World Wide Web Computing Grid. Distributed Production Environment of Physics Data Processing. http://lcg.web.cern.ch/LCGMessage Passing Interface Forum: MPI: A message-passing interface standard (2003), http://www.mpi-forum.org/Gropp, W., et al.: MPI: The Complete Reference. MIT Press, Cambridge (1998)Drepper, U., Molnar, I.: The Native POSIX Thread Library for Linux (2003), http://people.redhat.com/drepper/nptl-design.pdfFrank, E., Hall, M., L.T.: Weka 3: Data Mining Software in Java (2005), http://www.cs.waikato.ac.nz/ml/wek
CloudMon: a resource-efficient IaaS cloud monitoring system based on networked intrusion detection system virtual appliances
The networked intrusion detection system virtual appliance (NIDS-VA), also known as virtualized NIDS, plays an important role in the protection and safeguard of IaaS cloud environments. However, it is nontrivial to guarantee both of the performance of NIDS-VA and the resource efficiency of cloud applications because both are sharing computing resources in the same cloud environment. To overcome this challenge and trade-off, we propose a novel system, named CloudMon, which enables dynamic resource provision and live placement for NIDS-VAs in IaaS cloud environments. CloudMon provides two techniques to maintain high resource efficiency of IaaS cloud environments without degrading the performance of NIDS-VAs and other virtual machines (VMs). The first technique is a virtual machine monitor based resource provision mechanism, which can minimize the resource usage of a NIDS-VA with given performance guarantee. It uses a fuzzy model to characterize the complex relationship between performance and resource demands of a NIDS-VA and develops an online fuzzy controller to adaptively control the resource allocation for NIDS-VAs under varying network traffic. The second one is a global resource scheduling approach for optimizing the resource efficiency of the entire cloud environments. It leverages VM migration to dynamically place NIDS-VAs and VMs. An online VM mapping algorithm is designed to maximize the resource utilization of the entire cloud environment. Our virtual machine monitor based resource provision mechanism has been evaluated by conducting comprehensive experiments based on Xen hypervisor and Snort NIDS in a real cloud environment. The results show that the proposed mechanism can allocate resources for a NIDS-VA on demand while still satisfying its performance requirements. We also verify the effectiveness of our global resource scheduling approach by comparing it with two classic vector packing algorithms, and the results show that our approach improved the resource utilization of cloud environments and reduced the number of in-use NIDS-VAs and physical hosts.The authors gratefully acknowledge the anonymous reviewers for their helpful suggestions and
insightful comments to improve the quality of the paper. The work reported in this paper has been
partially supported by National Nature Science Foundation of China (No. 61202424, 61272165,
91118008), China 863 program (No. 2011AA01A202), Natural Science Foundation of Jiangsu Province
of China (BK20130528) and China 973 Fundamental R&D Program (2011CB302600)
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster
The NEMO High Performance Computing Cluster at the University of Freiburg has
been made available to researchers of the ATLAS and CMS experiments. Users
access the cluster from external machines connected to the World-wide LHC
Computing Grid (WLCG). This paper describes how the full software environment
of the WLCG is provided in a virtual machine image. The interplay between the
schedulers for NEMO and for the external clusters is coordinated through the
ROCED service. A cloud computing infrastructure is deployed at NEMO to
orchestrate the simultaneous usage by bare metal and virtualized jobs. Through
the setup, resources are provided to users in a transparent, automatized, and
on-demand way. The performance of the virtualized environment has been
evaluated for particle physics applications
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
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