Finding order in chaos: A behavior model of the whole grid

Concurrency and Computation: Practice and Experience

Volumn 22, Issue 11, 2010, Pages 1386-1415

  Montes, Jesús ^a   Sánchez, Alberto ^b   Valdés, Julio J ^c   Pérez, María S ^d   Herrero, Pilar ^d  

^a Madrid Supercomputing and Visualization Center   (Spain)

^b UNIVERSIDAD REY JUAN CARLOS   (Spain)

^c NATIONAL RESEARCH COUNCIL CANADA   (Canada)

^d UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Data mining; Grid behavior; Grid computing; Large scale dsitributed systems; Modelling

Indexed keywords

CHAOS THEORY; DATA MINING; DISTRIBUTED DATABASE SYSTEMS; LARGE SCALE SYSTEMS; MODELS;

BEHAVIOR MODEL; CASE-STUDIES; GLOBAL BEHAVIORS; GRID BEHAVIOR; GRID MANAGEMENT; LARGE-SCALE DISTRIBUTED SYSTEM; MANAGEMENT MECHANISMS;

GRID COMPUTING;

EID: 77954464334     PISSN: 15320626     EISSN: 15320634     Source Type: Journal    
DOI: 10.1002/cpe.1490     Document Type: Article

References (56)

1. Foster I, Kesselman C (eds.). The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers: Los Altos, CA, 2004.
2. Foster I. What is the grid? A three point checklist. Grid Today 2002; 1(6).
3. Buyya R, Abramson D, Giddy J. Nimrod/g: An architecture for a resource management and scheduling system in a global computational grid. HPC Asia, Beijing, China, vol.1, 14-17 May 2000; 283-289.
4. Krauter K, Buyya R, Maheswaran M. A taxonomy and survey of grid resource management systems for distributed computing. Software Practice and Experience 2002; 32(2):135-164. (Pubitemid 34138417)
5. Siddiqui M, Fahringer T. GridARM: Askalon's grid resource management system. Advances in Grid Computing-EGC 2005-Revised Selected Papers. (Lecture Notes in Computer Science, vol.3470), Amsterdam, Netherlands. Springer: Berlin, June 2005; 122-131. ISBN 3-540-26918-5.
6. Dongarra JJ, Gentzsch W. (eds.). Computer Benchmarks. Elsevier Science Publishers B. V.: Amsterdam, The Netherlands, 1993.
7. Dikaiakos MD. Grid benchmarking: Vision, challenges, and current status: Research articles. Concurrency and Computation: Practice and Experience 2007; 19(1):89-105.
8. Frumkin M, Van der Wijngaart RF. NAS Grid benchmarks: A tool for grid space exploration. Cluster Computing 2002; 5(3):247-255.
9. Tsouloupas G, Dikaiakos MD. Gridbench: A tool for the interactive performance exploration of grid infrastructures. Journal of Parallel and Distributed Computing 2007; 67(9):1029-1045.
10. Bratosin C, van der Aalst WMP, Sidorova N, Trcka N. A reference model for grid architectures and its analysis. OTM Conferences (1). (Lecture Notes in Computer Science, vol.5331), Meersman R, Tari Z (eds.). Springer: Berlin, 2008; 898-913.
11. Németh ZN, Sunderam V. A formal framework for defining grid systems. CCGRID '02: Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, Washington, DC, U.S.A. IEEE Computer Society: Silver Spring, MD, 2002; 202.
12. Nemeth ZN, Sunderam V. Characterizing grids: Attributes, definitions, and formalisms. Journal of Grid Computing 2003; 1(1):9-23.
13. Gurevich Y. Evolving Algebras: An attempt to discover semantics. EATCS Bulletin, vol.43. European Association for Theoretical Computer Science, 1991; 264-284.
14. Palatin N, Leizarowitz A, Schuster A, Wolff R. Mining for misconfigured machines in grid systems. KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: New York, NY, U.S.A., 2006; 687-692.
15. Hopcroft JE, Motwani R, Ullman JD. Introduction to Automata Theory, Languages, and Computation (2nd edn). Addison-Wesley: Reading, MA, 2000.
16. Carroll J, Long D. Theory of Finite Automata with an Introduction to Formal Languages. Prentice-Hall, Inc.: Upper Saddle River, NJ, U.S.A., 1989.
17. Kephart JO, Chess DM. The vision of autonomic computing. Computer 2003; 36(1):41-50.
18. Sánchez A. Autonomic high performance storage for grid environments based on long term prediction. PhD Thesis, Universidad Politécnica de Madrid, 2008.
19. GMonE: Grid Monitoring Environment. Available at: http://laurel.datsi.fi. upm.es/proyectos/globem/gmone/ [March 2009] [Online].
20. Schopf JM, D'Arcy M, Miller N, Pearlman L, Foster I, Kesselman C. Monitoring and discovery in a web services framework: Functionality and performance of the globus toolkit's MDS4. Technical Report ANL/MCS-P1248-0405, Argonne National Laboratory, 2005.
21. MonALISA-Monitoring the Grid since 2001. Available at: http://monalisa.cacr.caltech.edu/ [March 2009] [Online].
22. Newman HB, Legrand IC, Bunn JJ. A distributed agent-based architecture for dynamic services. CHEP 2001, Beijing, 2001.
23. Newman HB, Legrand IC, Galvez P, Voicu R, Cirstoiu C. MonALISA: A distributed monitoring service architecture. 2003 Computing in High Energy and Nuclear Physics (CHEP03). La Jolla: CA, U.S.A., 2003.
24. Wolski R, Spring TN, Hayes J. The network weather service: A distributed resource performance forecasting service for metacomputing. Future Generation Computer Systems 1999; 15(5-6):757-768.
25. Wolski R. Experiences with predicting resource performance on-line in computational grid settings. SIGMETRICS-Performance Evaluation Review 2003; 30(4):41-49.
26. Network Weather Service: Introduction. Available at: http://nws.cs.ucsb. edu/ewiki/ [March 2009] [Online].
27. Ganglia distributed monitoring and execution system. Available at: http://ganglia.info/ [March 2009] [Online].
28. Valdés JJ. Virtual reality representation of relational systems and decision rules. Theory and Application of Relational Structures as Knowledge Instruments, Hajek P (ed.), Meeting of the COST Action, vol.274, Prague, 2002.
29. Valdés JJ. Virtual Reality Representation of Information Systems and Decision Rules (Lecture Notes in Artificial Intelligence, vol.2639). Springer: Berlin, 2003; 615-618.
30. Chandon JL, Pinson S. Analyse Typologique. Théorie et Applications. Masson: Paris, 1981.
31. Borg I, Lingoes J. Multidimensional Similarity Structure Analysis. Springer: Berlin, 1987.
32. Valdés JJ. Similarity-based heterogeneous neurons in the context of general observational models. Neural Network World 2002; 12(5):499-508.
33. Kruskal J. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964; 29:1-27.
34. Sammon JW. A non-linear mapping for data structure analysis. IEEE Transactions on Computers 1969; C18:401-408.
35. Jain AK, Mao J. Artificial neural networks for nonlinear projection of multivariate data. 1992 IEEE Joint Conference on Neural Networks, Baltimore, MD, 1992; 335-340.
36. Valdés JJ. Building virtual reality spaces for visual data mining with hybrid evolutionary-classical optimization: Application to microarray gene expression data. 2004 IASTED International Joint Conference on Artificial Intelligence and Soft Computing, ASC'2004, Marbella, Spain. ACTA Press: Anaheim, U.S.A., September 2004; 161-166.
37. Valdés JJ, Barton AJ. Virtual reality visual data mining with nonlinear discriminant neural networks: Application to leukemia and Alzheimer gene expression data. Proceedings of the IJCNN'05 International Joint Conference on Neural Networks, Montreal, Canada, 31 July-4 August 2005.
38. Han J, Kamber M. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann: Los Altos, CA, September 2000.
39. MacQueen JB. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, Le Cam LM, Neyman J (eds.). University of California Press: Berkeley, CA, 1967; 281-297.
40. Johnson S. Hierarchical clustering schemes. Psychometrika 1967; 32(3):241-254.
41. D'Andrade R. U-statistic hierarchical clustering. Psychometrika 1978; 43(1):59-67.
42. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 1977; 39(1):1-38.
43. Heyer LJ, Kruglyak S, Yooseph S. Exploring expression data: Identification and analysis of coexpressed genes. Genome Research 1999; 9(11):1106-1115.
44. Ester M, Kriegel H-P, Jörg S, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise, 1996.
45. Haykin S. Neural Networks: A Comprehensive Foundation. Macmillan: New York, 1994.
46. Quinlan RJ. C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning). Morgan Kaufmann: Los Altos, CA, 1993.
47. Rand WM. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 1971; 66(336):846-850.
48. Fowlkes EB, Mallows CL. A method for comparing two hierarchical clusterings. Journal of the American Statistical Association 1983; 78(383):553-584.
49. Planetlab-An open platform for developing, deploying and accessing planetary-scale services. Available at: http://www.planet-lab.org/ [March 2009] [Online].
50. EGEE Portal: Enabling Grids for E-sciencE. Available at: http://www.eu-egee.org/ [March 2009] [Online].
51. CERN European Organization for Nuclear Research. EGEE-Alice Job Summary. Available at: http://dashb-alice.cern.ch/dashboard/request.py/jobsummary [February 2009] [Online].
52. CERN European Organization for Nuclear Research. EGEE-Atlas Dashboard. Available at: http://dashb-atlas-prodsystest. cern.ch/dashboard/request.py/ summary [December 2008] [Online].
53. CERN European Organization for Nuclear Research. EGEE-Atlas Job Summary. Available at: http://dashb-atlas-job.cern.ch/dashboard/request.py/jobsummary [February 2009] [Online].
54. CERN European Organization for Nuclear Research. EGEE-CMS Job Summary. Available at: http://lxarda09.cern.ch/dashboard/request.py/jobsummary [February 2009] [Online].
55. Buyya R, Murshed M. GridSim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience 2002; 14(13-15):1175-1220.
56. CoMon-A Monitoring Infrastructure for PlanetLab. Available at: http://comon.cs.princeton.edu/ [March 2009] [Online].