An On-Line Approach for Classifying and Extracting Application Behavior on Linux

High-Performance Computing: Paradigm and Infrastructure

Volumn , Issue , 2006, Pages 381-401

  Senger, Luciano José ^a   Fernandes de Mello, Rodrigo ^b   Santana, Marcos José ^b   Helena, Regina ^b   Santana, Carlucci ^b   Yang, Laurence T ^c  

^a STATE UNIVERSITY OF PONTA GROSSA   (Brazil)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

^c ST FRANCIS XAVIER UNIVERSITY   (Canada)

Author keywords

Adaptive resonance theory; Application behavior; Load balancing; Neural networks

Indexed keywords

EID: 84889391866     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/0471732710.ch20     Document Type: Chapter

References (62)

1. Y. Amir, An opportunity cost approach for job assignment in a scalable computing cluster, IEEE Transactions on Parallel and Distributed Systems, 11(7), 760-768, July 2000.
2. T. Anderson, D. Culler, and D. Patterson, A case for NOW (networks of workstations), IEEE Micro, 15(1), 54-64, 1995.
3. A. C. Arpaci-Dusseau, D. E. Culler, and M. Mainwaring, Scheduling with implicit information in distributed systems, in Proceedings of ACM SIGMETRICS' 98, pp. 233-248, August 1998.
4. I. Bachelder,A.Waxman, and M. Seibert, Aneural system formobile robot visual place learning and recognition, in Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching, vol. 807, pp. 198-212, Berlin, 1993. Springer-Verlag.
5. A. Beguelin, A. Gueist, J. Dongarra, W. Jiang, R. Manchek, and V. Sunderam, PVM: Parallel Virtual Machine: User's Guide and tutorial for Networked Parallel Computing, MIT Press, 1994.
6. D. P. Bovet and M. Cesati, Understanding the Linux Kernel, O'Reilly, October 2000.
7. R. Buyya, High Performance Cluster Computing-Architecture and Systems, vol. 1, Prentice-Hall, 1999.
8. G. A. Carpenter, M. N. Gjaja, S. Gopal, and C. E. Woodcock, ART neural networks for remote sensing: Vegetation classification from Lansat TM and terrain data, IEEE Transactions on Geoscience and Remote Sensing, 35(2), 308-325, March 1997.
9. G. A. Carpenter and S. Grossberg, The ART of adaptive pattern recognition by a self-organizing neural network, Computer, (21), 77-88, 1988.
10. G. A. Carpenter and S. Grossberg, ART 2: Self-organization of stable category recognition codes for analog input patterns, Applied Optics, 26(23), 4919-4930, 1989.
11. G. A. Carpenter, S. Grossberg, and D. B. Rosen, ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition, Neural Networks, 4, 4934-504, 1991.
12. T. L. Casavant and J. G. Kuhl, A Taxonomy of scheduling in general-purpose distributed computing systems, IEEE Transactions on Software Engineering, 14(2), 141-154, February 1988.
13. J. Corbalan, X. Martorell, and J. Labarta, improving gang scheduling through job performance analysis and malleability, in International Conference on Supercomputing, pp. 303-311, Sorrento, Italy, June 2001.
14. A. de P. Braga, T. B. Ludermir, and A. C. Carvalho, Redes Neurais Artificiais: Teoria e Aplicações, LTC, 2000.
15. M. V. Devarakonda and R. K. Iyer, Predictability of process resource usage: A measurement- based study on UNIX, IEEE Transactions on Software Engineering, 15(12), 1579-1586, 1989.
16. L. G. Fagundes, R. F. Mello, and C. E. Moron, An environment for generating applications involving remote manipulation of parallel machines, in IEEE 7TH International Workshop on Parallel and Distributed Real-Time Systems (WPDRTS/99), Puerto Rico, Apr. 1999.
17. D. G. Feitelson, L. Rudolph, U. Schwiegelshohn, K. C. Sevcik, and P. Wong, Theory and practice in parallel job scheduling, in Job Scheduling Strategies for Parallel Processing, pp. 1-34. Springer-Verlag, 1997.
18. D. Ferguson, Y. Yemini, and C. Nikolau, Microeconomic algorithms for load balancing in distributed computer systems, in VII International Conference on Distributed Computer Systems, pp. 491-499, 1988.
19. S. M. Figueira and F. Berman, A slowdown model for applications executing on timeshared clusters of workstations, IEEE Transactions on Parallel and Distributed Systems, 12(6), 653-670, June 2001.
20. A. Filippidis, L. C. Jain, and P. Lozo, Degree of familiarity ART2 in knowledgebased landmine detection, IEEE Transactions on Neural Networks, 10(1), 186-193, January 1999.
21. M. J. Flynn and K. W. Rudd, Parallel architectures, ACMComputing Surveys, 28(1), 68-70, 1996.
22. K. Gan and K. Lua, Chinese character classification using adaptive resonance network, Pattern Recognition, 25, 877-888, 1992.
23. R. Gibbons, A historical application profiler for use by parallel schedulers, in Job Scheduling Strategies for Parallel Processing, pp. 58-77. Springer-Verlage, 1997.
24. R. C. Gonzalez and R. E. Woods, Processamento de Imagens Digitais, Edgard Blücher, 2000.
25. K. K. Goswami, M. Devarakonda, and R. K. Iyer, Prediction-based dynamic load-sharing heuristics, IEEE Transactions on Parallel and Distributed Systems, 4(6), 638-648, June 1993.
26. S. Haykin, Neural Networks-A Compreensive Foundation, Prentice-Hall, 1994.
27. J. He, A.-H. Tan, and C.-L. Tan, Modified Art 2A growing network capable of generating a fixed number of nodes, IEEE Transactions on Neural Networks, 15(3), 728-737, 2004.
28. R. L. Henderson, Job scheduling under the portable batch system, in Job Scheduling Strategies for Parallel Processing (IPPS·95), pp. 280-94, Apr. 1995.
29. T. Hsu, Task allocation on a network of processors, IEEE Transactions on Computers, 49(12), 1339-1353, 2000.
30. C. Hui and S. T. Chanson, Hydrodynamic load balancing, IEEE Transactions on Parallel and Distributed Systems, 10(11), 1118-1137, Nov. 1999.
31. S. Keyvan and L. C. Rabelo, Sensor signal analysis by neural networks for surveillance in nuclear reactors, IEEE Transactions on Nuclear Science, 39(2), Apr. 1992.
32. P. Krueger and M. Livny, The diverse objectives of distributed scheduling policies, in Seventh International Conference Distributed Computing Systems, pp. 242-249, IEEE Computer Science Press, 1987.
33. P. K. K. Loh, How network topology affects dynamic load balancing, IEEE Parallel & Distributed Technology, 4(3), 25-35, 1996.
34. R. F. Mello, L. C. Trevelin, M. S. Paiva, and L. T. Yang, Comparative study of the server- initiated lowest algorithm using a load balancing index based on the process behavior for heterogeneous environment, in Networks, Software Tools and Applications, Kluwer, 2004.
35. R. F. Mello, L. C. Trevelin, M. S. Paiva, and L. T. Yang, Comparative analysis of the prototype and the simulator of a new load balancing algorithm for heterogeneous computing environments, in International Journal Of High Performance Computing And Networking, 2004.
36. R. F. Mello, L. C. Trevelin, M. S. Paiva, and L. T. Yang, Comparative study of the serverinitiated lowest algorithm using a load balancing index based on the process behavior for heterogeneous environment, in Networks, Software Tools and Applications, Kluwer, 2004.
37. R. R. Mello and C. E. Moron, Análise dos mecanismos de tempo real em java, in Workshop De Tempo Real do XVII Sinpósio Brasileiro de Redes De Computadores, vol. 1, pp. 43-50, Salvador, Bahia, Brazil, May 1999.
38. Message Passing Interface Forum, MPI-2: Extensions to the Message Passing Interface, July 1997.
39. M. Mitzenmacher, How useful is old information? IEEE Transactions on Parallel and Distributed Systems, 11(1), 6-20, Jan. 2000.
40. M. Mitzenmacher, The power of two choices in randomized load balancing, IEEE Transactions on Parallel and Distributed Systems, 12(10), 1094-1104, Oct. 2001.
41. M. Neeracher, Scheduling for Heterogeneous Opportunistic Workstation Clusters, PhD thesis, Swiss Federal Institute of Technology, Zurich, 1999.
42. L. M. Ni, C. Xu, and T. B. Gendreau, A distributed drafting algorithm for load balancing, IEEE Transactions on Software Engineering, 11(10), 1153-1161, 1985.
43. M. A. Palis, J. Liou, and D. S. L. Wei, Task clustering and scheduling for distributed memory parallel architectures, IEEE Transactions on Parallel and Distributed Systems, 7(1), 46-55, Jan. 1996.
44. J. Ryou and J. S. K. Wong, A task migration algorithm for load balancing in a distributed system, in XXII Annual Hawaii International Conference on System Sciences, pp. 1041-1048, Jan. 1989.
45. L. J. Senger, M. J. Santana, and R. H. C. Santana, Uma nova abordagem para a aquisição de conhecimento sobre o comportamento de aplicações paralelas na utilização de recursos, in Proceedings of WORKCOMP, pp. 79-85, São José dos Campos, São Paulo, Brazil, October 2002.
46. L. J. Senger, M. J. Santana, and R. H. C. Santana, A new approach fo acquiring knowledge of resource usage in parallel applications, in Proceedings of International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS'2003), pp. 607-614, 2003.
47. L. J. Senger, M. J. Santana, and R. H. C. Santana, Using runtime measurements and historical traces for acquiring knowledge in parallel applications, in International Conference on Computational Science (ICCS). Springer-Verlag, 2004.
48. N. G. Shivaratri, P. Krueger, and M. Singhal, Load distributing for locally distributed systems, IEEE Computer, 25(12), 33-44, 1992.
49. F. A. B. D. Silva and I. D. Scherson, Improving parallel job scheduling using runtime measurements, in D. G. Feitelson and L. Rudolph (Eds.), Job Scheduling Strategies for Parallel Processing, pp. 18-38. Springer-Verlag, 2000.
50. W. Smith, I. T. Foster, and V. E. Taylor, Predicting application run times using historical information, LNCS J459, 122-142, 1998.
51. T. T. Y. Suen and J. S. K. Wong, Efficient task migration algorithm for distributed systems, IEEE Transactions on Parallel and Distributed Systems, 3(4), 488-499, 1992.
52. A. S. Tanenbaum, Modern Operating Systems. Prentice-Hall, 1992.
53. M. Tiemeyer and J. S. K. Wong, A task migration algorithm for heterogeneous distributed computing systems, The Journal of Systems and Software, 41(3), 175-188, 1998.
54. A. Ultsch, Self-organising neural networks for monitoring and knowledge acquisition of a chemical process, in Proceedings of ICANN-93, pp. 864-867, 1993.
55. N. Vlajic and H. C. Card, Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering, IEEE Transactions on Neural Networks, 12(5), September 2001.
56. J. R. Whiteley and J. F. Davis, Qualitative interpretation of sensor patterns, IEEE Expert, 8, 54-63, 1993.
57. James R. Whiteley and James F. Davis, Observations and problems applying ART2 for dynamic sensor pattern interpretation, IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 26(4), 423-437, 1996.
58. M. H. Willebeek-Lemair and A. P. Reeves, Strategies for dynamic load balancing on highly parallel computers, IEEE Transactions on Parallel and Distributed Systems, 4(9), 979-993, Sept. 1993.
59. M. Wu, On runtime parallel scheduling for processor load balancing, IEEE Transactions on Parallel and Distributed Systems, 8(2), 173-186, Feb. 1997.
60. Y. Zhang, Impact of workload and system parameters on next generation cluster schedulingmechanisms, IEEE Transactions on Parallel and Distributed Systems, 12(9), 967-985, Sep. 2001.
61. S. Zhou and D. Ferrari, An experimental study of load balancing performance, Technical Report UCB/CSD 87/336, PROGRES Report N.o 86.8, Computer Science Division (EECS), University of California, Berkeley, Jan. 1987.
62. A. Y. Zomaya and Y. TEH, Observations on using genetic algorithms for dynamic loadbalancing, IEEE Transactions on Parallel and Distributed Systems, 12(9), 899-911, Sept. 2001.