Uncertainty-tolerant scheduling strategies for grid computing: knowledge-based techniques with bio-inspired learning

• Autorzy: García-Galán S , Prado R , Expósito J E
• Języki publikacji: EN

Abstrakty

EN

Nowadays, diverse areas in science as high energy physics, astronomy or climate research are increasingly relying on experimental studies addressed with hard computing simulations that cannot be faced with traditional distributed systems. In this context, grid computing has emerged as the new generation computing platform based on the large-scale cooperation of resources. Furthermore, the use of grid computing has also been extended to several technology, engineering or economy areas such as financial services and construction engineering that demand high computer capabilities. Nevertheless, a major issue in the sharing of resources is the scheduling problem in a high-dynamic and uncertain environment where resources may become available, inactive or reserved over time according to local policies or systems failures. In this paper, a review of scheduling strategies dealing with uncertainty in systems information by the application of techniques such as fuzzy logic, neural networks or evolutionary algorithms is presented. Furthermore, this work is centered on the study of scheduling strategies based on fuzzy rulebased systems given their flexibility and ability to adapt to changes in grid systems. These knowledge-based strategies are founded on a fuzzy characterization of the system state and the application of the scheduler knowledge in the form of fuzzy rules to cope with the imprecise environment. Obtaining good rules also arises as a challenging problem. Hence, the main learning methods that allow the improvement and adaptation of the expert schedulers are introduced.

Słowa kluczowe

EN

evolutionary algorithms; grid computing; fuzzy rule-based systems

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2013
• Tom: Vol. 18, no. 1
• Strony: 37--44
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

García-Galán S

• Telecommunication Engineering Department. University of Jaén. Alfonso X El Sabio, 23700, Linares (Spain)

autor

Prado R

• Telecommunication Engineering Department. University of Jaén. Alfonso X El Sabio, 23700, Linares (Spain)

autor

Expósito J E

• Telecommunication Engineering Department. University of Jaén. Alfonso X El Sabio, 23700, Linares (Spain)

Bibliografia

• [1] I. Foster, C. Kesselman, The Grid 2, blueprint for a New Computing Infrastructure, Morgan Kaufmann Publishers Inc, 2003
• [2] F. Berman, The Grid, blueprint for a Future Computing Infrastructure, Morgan Kaufmann Publishers, 1998
• [3] M. Baker, R. Perrott, Position Papers,Workshop Report, Workshop on Grid Technologies, pp. 16-17, 2000
• [4] J.M. Gutierrez, Iniciativa de Programa de e-Ciencia e IRIS-GRID, Area Tematica de Meteorologia, Tech. Report, 2004
• [5] J.R. Valverde, Propuesta para la Creacion de un Programa de e-Ciencia, Area Tematica de Bioinformatica, Tech. Report, 2004
• [6] J.M. Marin, S.B. Camara, Las Tecnologías Grid de la Información como Nueva Herramienta Empresarial, Septem Ediciones, 2008
• [7] I. Foster, A. Iamnitchi, On death, taxes, and the convergence of peer to-peer and grid computing. In , 2nd International Workshop on Peer-to-Peer Systems (IPTPS 03), Berkeley, USA, pp. 118-128, 2003
• [8] K. Christodoulopoulos, V. Sourlas, I. Mpakolas, E. Varvarigos, A comparison of centralized and distributed meta-scheduling architectures for computation and communication tasks in Grid networks, Computer Communications, pp. 1172-1184, 2009
• [9] M. Kalantari, M. Akbari, A parallel solution for scheduling of real time applications on grid environments, Future Gener. Comput. Syst, pp. 704-716, 2009
• [10] F. Xhafa, A. Abraham, Computational models and heuristic methods for Grid scheduling problems, Future Generation Computer Systems, pp. 608-621, 2010
• [11] F. Xhafa, A. Abraham, : Meta-heuristics for grid scheduling problems, Metaheuristics for Scheduling: Distributed Computing Environments, Studies in Computational Intelligence, pp. 1-37, 2008
• [12] O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases,World Scientiffic Pub Co Inc, 2001
• [13] C. Franke, F. Hoffmann, J. Lepping, U. Schwiegelshohn, Development of scheduling strategies with Genetic Fuzzy systems, Appl. Soft Comput, pp. 706-721, 2008
• [14] S. Garcia-Galan, R.P. Prado, J.E. Munoz-Exposito, Fuzzy Scheduling with Swarm Intelligence-Based Knowledge Acquisition for Grid Computing, Engineering Applications of Artificial Intelligence, vol. 25, pp. 359-375, 2012
• [15] R.P. Prado, S. Garcia-Galan, J.E. Munoz-Exposito, A.J. Yuste, Knowledge acquisition in fuzzy rule based systems with particle swarm optimization, Fuzzy Systems, IEEE Transactions on, Vol. 18, No. 6, pp. 1083-1097, 2010
• [16] R.P. Prado, S. Garcia-Galan, J.E. Munoz Exposito, A.J. Yuste, Genetic Fuzzy Rule-Based Scheduling System for Grid Computing in Virtual Organizations, Soft Computing, Vol. 15, pp. 1255-1271, 2011
• [17] S. Garcia-Galan, R.P. Prado, J.E. Munoz Exposito, Swarm Fuzzy Systems: Knowledge Acquisition in Fuzzy Systems and its Applications in Grid Computing, IEEE-Transactions on Knowledge and Data Engineering, In press, 2013
• [18] S. Farzi, Efficient job scheduling in grid computing with modified artificial fish swarm algorithm, International Journal of Computer Theory and Engineering, pp. 13-18, 2009
• [19] D.A. Menasca, E. Casalicchio, Qos in grid computing, IEEE Internet Computing, Vol. 8, No. 4, pp. 85-87, 2004
• [20] J. Carretero, B. Dorrosonro, E. Alba, F. Xhafa, A tabu search algorithm for scheduling independent jobs in computational grids, Computing and informatics, Vol. 28, No. 2, pp. 237-250, 2009
• [21] T.D. Braun, H.J. Siegel, N. Beck, L.L. Boloni, M. Maheswaran, A.I. Reuther, J.P. Robertson, M.D. Theys, B. Yao, D. Hensgen, R.F. Freund, A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems, Journal of Parallel and Distributed Computing, Vol. 61, No. 6, pp. 810-837, 2001
• [22] L.A. Zadeh, Fuzzy Sets, Information and Control, pp. 338-353, 1965
• [23] J. Zhou, K. Yu, C. Chou, L. Yang, Z. Luo, A Dynamic Resource Broker and Fuzzy Logic Based Scheduling Algorithm in Grid Environment, Lecture Notes in Computer Science, Vol. 4431, pp. 604-613, 2007
• [24] K. Yu, Z. Luo, C. Chou, C. Chen, J. Zhou, A fuzzy neural network based scheduling algorithm for job assignment on computational grids, Lecture Notes in Computer Science, Vol. 4658, pp. 533-542, 2007
• [25] X. Hao, Y. Dai, B. Zhang, T. Chen, L. Yang, QoSDriven Grid Resource Selection Based on Novel Neural Networks, Lecture Notes in Computer Science, Vol. 3947, pp. 456-465, 2006
• [26] H. Liu, A. Abraham, A.E. Hassanien, Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm, Future Gener. Comput. Syst., pp. 1336-1343, 2010
• [27] C. Franke, J. Lepping, U. Schwiegelshohn, On advantages of scheduling using genetic fuzzy systems, Lecture Notes in Computer Science, pp. 68-93, 2007
• [28] B. Song, C. Ernemann, R. Yahyapour, User groupbased workload analysis and modelling, Cluster Computing and the Grid. CCGrid 2005, IEEE International Symposium on, Vol. 2, pp. 953-961, 2005
• [29] C. Jiang, C. Wang, X. Liu, Y. Zhao, A Fuzzy Logic Approach for Secure and Fault Tolerant Grid Job Scheduling, Lecture Notes in Computer Science, Vol. 4610, pp. 549-558, 2007
• [30] L.B. Booker, D.E. Goldberg, J.H. Holland, Classifier systems and genetic algorithms, Artif. Intell, pp. 235-282, 1989
• [31] S. Smith, A. Frederick, Learning system based on genetic adaptive algorithms, University of Pittsburgh, 1980
• [32] R. Eberhart, Y. Shi, Evolving artificial neural networks, In Proc. Int. Conf. on Neural Networks and Brain, Beijing, PR China, pp. 5-13, 1998