350 rub
Journal Neurocomputers №6 for 2011 г.
Article in number:
Workload node balancing in decentralized network system with imperfect information
Authors:
N. O. Amelina
Abstract:
This paper presents workload node balancing in decentralized network system with imperfect information about the current state of nodes and changing relationship structure. Workload node balancing problem reformulates as consensus problem in noisy model with switched topology. For solving this problem the algorithm of stochastic approximation is offered. The simulation results of the algorithm are made.
Pages: 56-63
References
  1. Fax, R., Murray, M., Information flow and cooperative control of vehicle formations // IEEE Trans. Automat. Contr. Sept. 2004. V. 49. P. 1465-1476.
  2. Toner, J., Tu, Y., Flocks, herds, and schools: a quantitative theory of flocking - Phys. Rev. E. Oct. 1998. V. 58. № 4. P. 4828-4858.
  3. Cortes, J., Bullo,F., Coordination and geometric optimization via distributed dynamical systems SIAM J. Control Optim., May 2003.
  4. Paganini, F., Doyle, J., Low , S., Scalable laws for stable network congestion control // Presented at the Int. Conf. Decision and Control, Orlando, FL, Dec. 2001.
  5. Jadbabaie, Lin, J., and Morse,A. S., Coordination of groups of mobile autonomous agents using nearest neighbor rules // IEEE Trans. Automat. Contr. June 2003. V. 48. P. 988-1000.
  6. Olfati-Saber, R. and Murray,R. M., Consensus problems in networks of agents with switching topology and time-delays // IEEE Trans. Automatic Control. Sep. 2004. V. 49. P. 1520-1533.
  7. Ren, W. and Beard,R. W., Consensus seeking in multiagent systems under dynamically changing interaction topologies // IEEE Trans. Automat. Control. 2005. V. 50. № 5. P. 655-661.
  8. Aysal T. C. and BarnerK. E. Convergence of consensus models with stochastic disturbances // IEEE Trans. Info. Theory. 2010. V. 56. № 8. P. 4101-4113.
  9. Xiao, L., Boyd, S., and Kim,S.-J., Distributed average consensus with least-mean-square deviation // Journal of Parallel and Distributed Computing. 2007. V. 67. P. 33-46.
  10. Schizas, D., Ribeiro, A., and GiannakisG. B., Consensus in ad hoc WSNs with noisy links-part I: distributed estimation of deterministic signals // IEEE Trans. Signal Process. Jan. 2008. V. 56. P. 350-364.
  11. Cucker, F. and Mordecki,E., Flocking in noisy environments // J. Math. Pures Appl. 2008. V. 89. № 3. P. 278-296.
  12. Kashyap, T. Basar, and Srikant,R., Quantized consensus // Automatica. 2007. V. 43. P. 1192-1203.
  13. Huang,M., Stochastic Approximation for Consensus with General Time-Varying Weight Matrices // Proc. the 49th IEEE CDC, Atlanta, GA, USA, Dec. 2010. P. 7449-7454.
  14. Tsitsiklis, N., Bertsekas, D. P., and Athans,M., Distributed asynchronous deterministic and stochastic gradient optimization algorithms // IEEE Trans. Autom. Contr. 1986. V. 31. № 9. P. 803-812.
  15. Huang, M. and Manton,J. H., Coordination and consensus of networked agents with noisy measurements: stochastic algorithms and asymptotic behavior // SIAM J. Control Optim. 2009. V. 48. № 1. P. 134-161.
  16. Kar, S. and Moura,J. M. F., Distributed consensus algorithms in sensor networks with imperfect communication: link failures and channel noise // IEEE Trans. Sig. Process. 2009. V. 57. № 1. P. 355-369.
  17. Li, T. and Zhang,J.-F., Mean square average-consensus under measurement noises and fixed topologies // Automatica. 2009. V. 45. № 8. P. 1929-1936.
  18. Gharesifard, B. and Cortes,J., Distributed strategies for generating weight-balanced and doubly stochastic digraphs // SIAM J. Contr. Optim. Nov. 2010.
  19. Granichin, O., Vakhitov, A., and Vlasov, V., Adaptive control of SISO plant with time-varying coefficients based on random test perturbation // In Proc. of the 2010 American Control Conference, June 30 - July 02, 2010. Baltimore, MD, USA. P. 4004-4009.
  20. Вахитов А. Т., Граничин О. Н., Гуревич Л. С. Алгоритм стохастической аппроксимации с пробным возмущением на входе в нестационарной задаче оптимизации // Автоматика и телемеханика. 2009. № 11. C. 70-79.
  21. Granichin, O., Gurevich, L., and Vakhitov, A., Discrete-time minimum tracking based on stochastic approximation algorithm with randomized differences // Proc. of the Combined 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference. December 16-18, 2009, Shanghai, P.R. China. P. 5763-5767.
  22. Borkar, V. S., Stochastic Approximation. A Dynamical Systems Viewpoint - Cambridge University Press. 2008
  23. Граничин О. Н. Стохастическая оптимизация и системное программирование // Стохастическая оптимизация в информатике. 2010. 6. С. 3-44.
  24. Вахитов А. Т., Граничин О. Н., Паньшенсков М. А. Методы оценивания скорости передачи данных в грид // Нейрокомпьютеры: разработка, применение. 2009. № 11. C. 45 - 52.
  25. Фрадков А. Л. Схема скоростного градиента и его примнения в задачах адаптивного управления // Автоматика и телемеханика. 1979. № 9. C. 90 - 101.