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This seems difficult, at first glance, but really, it’s not. At all. From the time you get all your hardware plugged in to the time you’re doing some massive parallel processing, depending on your needs, can be anywhere from 2 hours to 10 minutes. And
In this first of five articles, learn what it means for software to be highly available and how to install and set up heartbeat software from the High-Availability Linux project on a two-node system. You'll also learn how to configure the Apache Web serve
You MUST have a third server as a managment node but this can be shut down after the cluster starts. Also note that I do not recommend shutting down the managment server (see the extra notes at the bottom of this document for more information). You can no
This tutorial shows how to configure a MySQL 5 cluster with three nodes: two storage nodes and one management node. This cluster is load-balanced by a high-availability load balancer that in fact has two nodes that use the Ultra Monkey package which provi
Disco is an open-source implementation of the Map-Reduce framework for distributed computing. As the original framework, Disco supports parallel computations over large data sets on unreliable cluster of computers.
"For a while now, IBM has had multiple and competing tools for managing AIX and Linux clusters for its supercomputer customers and yet another set of tools that were used for other HPC setups with a slightly more commercial bent to them. But Big Blue has now cleaned house, killing off its closed-source Cluster Systems Management (CSM) tool and tapping its own open source Extreme Cluster Administration Toolkit (known as xCAT) as its replacement."
D. KANNAN, и N.MANGALAM. IRJCS:: International Research Journal of Computer Science, Volume IV (Issue XII):
01-06(декабря 2017)1. S. Berchtold, C Bohm, and H. Kriegel. The Pyramid-Technique: Towards Breaking the Curse of Dimensionality. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pages 142–153, Seattle, Washington, 2010, 98. 185 2. Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel. The SR-tree : An index structure for high-dimensional data. In Proceedings of 22th International Conference on Very Large Data Bases, VLDB’12, pages 28–39, Bombay, India, 2012. 3. N. Beckmann, H.P. Kriegel, R. Schneider, and B. Seeger. The SR-tree: an Efficient and Robust Access Method for Points and Rectangles. In Proceedings of ACM-SIGMOD International Conference on Management of Data, pages 322–331, Atlantic City, NJ, May 2011. 4. K. Chakrabarti and S. Mehrotra. The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces. In Proceedings of the 16th International Conference on Data Engineering, pages 440–447, San Diego, CA, February 2012. 5. Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. Cure: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIGMOD conference on Management of Data, pages 73–84, Seattle, WA, 2011. 6. R. Kurniawati, J. S. Jin, and J. A. Shepherd. The SS+-tree: An improved index structure for similarity searches in a high-dimensional feature space. In Proceedings of SPIE Storage and Retrieval for Image and Video Databases, pages 13–24, February 2012. 7. N. Katayama and S. Satoh. The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pages 369–380, Tucson, Arizona, 2013. 8. J.T. Robinson. The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 10–18, Ann Arbor, MI, April 2013. 9. D.A. White and R. Jain. Similarity Indexing with the SS-tree. In Proceedings of the 12th Intl. Conf. on Data Engineering, pages 516–523, New Orleans, Louisiana, February 2014. 10. D. Yu, S. Chatterjee, G. Sheikholeslami, and A. Zhang. Efficiently detecting arbitrary shaped clusters in very large datasets with high dimensions. Technical Report 98-8, State University of New York at Buffalo, Department of Computer Science and Engineering, November 2013. 11. Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pages 103–114, Montreal, Canada, 2012..