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Mathematical Problems in Engineering
Volume 2012, Article ID 625861, 12 pages
http://dx.doi.org/10.1155/2012/625861
Research Article

Optimization of Resource Control for Transitions in Complex Systems

Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Splaiul Independentei 313, 060042 Bucharest, Romania

Received 7 March 2012; Accepted 2 April 2012

Academic Editor: Cristian Toma

Copyright © 2012 Florin Pop. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Toma, E. G. Bakhoum, and M. Li, “Propagation phenomena and transitions in complex systems: Efficient mathematical models,” Mathematical Problems in Engineering, vol. 2012, Article ID 429129, 3 pages, 2012. View at Publisher · View at Google Scholar
  2. E. G. Bakhoum and C. Toma, “Specific mathematical aspects of dynamics generated by coherence functions,” Mathematical Problems in Engineering, vol. 2011, Article ID 436198, 10 pages, 2011. View at Publisher · View at Google Scholar
  3. J. Kołodziej and F. Xhafa, “Integration of task abortion and security requirements in GA-based meta-heuristics for independent batch Grid scheduling,” Computers and Mathematics with Applications, vol. 63, no. 2, pp. 350–364, 2012. View at Publisher · View at Google Scholar
  4. Y. Huang, N. Bessis, P. Norrington, P. Kuonen, and B. Hirsbrunner, “Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm,” Future Generation Computer Systems. In press. View at Publisher · View at Google Scholar
  5. H. Abdu, H. Lutfiyya, and M. A. Bauer, “Optimizing management functions in distributed systems,” Journal of Network and Systems Management, vol. 10, no. 4, pp. 505–530, 2002. View at Publisher · View at Google Scholar
  6. T. L. Casavant and J. G. Kuhl, “A taxonomy of scheduling in general-purpose distributed computing systems,” IEEE Transactions on Software Engineering, vol. 14, no. 2, pp. 141–154, 1988. View at Google Scholar
  7. R. Armstrong, D. Hensgen, and T. Kidd, “The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions,” in Proceedings of the Seventh Hetero-geneous Computing Workshop (HCW '98), pp. 79–87, IEEE Computer Society, Washington, DC, USA, 1998.
  8. J. Wu, X. Xu, P. Zhang, and C. Liu, “A novel multi-agent reinforcement learning approach for job scheduling in Grid computing,” Future Generation Computer Systems, vol. 27, no. 5, pp. 430–439, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Wang, X.-S. Zhou, Q.-R. Liu, Z.-Y. Yang, and Y.-L. Wang, “An adaptive resource scheduling algorithm for computational grid,” in Proceedings of the IEEE Asia-Pacific Conference on Services Computing (APSCC '06), pp. 447–450, IEEE Computer Society, Washington, DC, USA, 2006. View at Publisher · View at Google Scholar
  10. B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, “Virtual infrastructure management in private and hybrid clouds,” IEEE Internet Computing, vol. 13, no. 5, pp. 14–22, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Zhao, K. Ramamritham, and J. A. Stankovic, “Preemptive scheduling under time and resource constraints,” IEEE Transactions on Computers, vol. 36, no. 8, pp. 949–960, 1987. View at Google Scholar · View at Scopus
  12. J. Koodziej and F. Xhafa, “Enhancing the genetic-based scheduling in computational grids by a structured hierarchical population,” Future Generation Computer Systems, vol. 27, no. 8, pp. 1035–1046, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Ma, Q. Yan, W. Liu, and C. Mengmeng, “A survey on grid task scheduling,” International Journal of Computer Applications in Technology, vol. 41, no. 3-4, pp. 303–309, 2011. View at Publisher · View at Google Scholar
  14. G. Logothetis, Specification, Modelling, Verification and Runtime Analysis of Real Time Systems, IOS Press, 2004.
  15. E. Fersman and W. Yi, “A generic approach to schedulability analysis of real-time tasks,” Nordic Journal of Computing, vol. 11, no. 2, pp. 129–147, 2004. View at Google Scholar
  16. G. C. Buttazzo, Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, Springer, 3rd edition, 2011.
  17. G. V. Iordache, M. S. Boboila, F. Pop, C. Stratan, and V. Cristea, “A decentralized strategy for genetic scheduling in heterogeneous environments,” Multiagent and Grid Systems, vol. 3, no. 4, pp. 355–367, 2007. View at Google Scholar
  18. C. Dobre, C. Stratan, and V. Cristea, “Realistic simulation of large scale distributed systems using monitoring,” in Proceedings of the 7th International Symposium on Parallel and Distributed Computing (ISPDC '08), pp. 434–438, IEEE Computer Society, Washington, DC, USA, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Pop, C. Dobre, G. Godza, and V. Cristea, “A simulation model for grid scheduling analysis and optimization,” in Proceedings of the International Symposium on Parallel Computing in Electrical Engineering (PARELEC '06), pp. 133–138, IEEE Computer Society, Washington, DC, USA, 2006. View at Publisher · View at Google Scholar
  20. S. Venugopal and R. Buyya, “An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids,” Journal of Parallel and Distributed Computing, vol. 68, no. 4, pp. 471–487, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Pop, C. Dobre, and V. Cristea, “Performance analysis of grid DAG scheduling algorithms using MONARC simulation tool,” in Proceedings of the 7th International Symposium on Parallel and Distributed Computing (ISPDC '08), pp. 131–138, IEEE Computer Society, Washington, DC, USA, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. F. Xhafa and A. Abraham, “Computational models and heuristic methods for Grid scheduling problems,” Future Generation Computer Systems, vol. 26, no. 4, pp. 608–621, 2010. View at Publisher · View at Google Scholar · View at Scopus