A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility

IEEE Transactions on Neural Networks and Learning Systems

Volumn 27, Issue 2, 2016, Pages 238-248

  Guo, Zhishan ^a   Baruah, Sanjoy K ^a  

^a Brooks Computer Science Building   (United States)

Author keywords

Neurodynamic optimization; NP hard problem; overloaded job set; real Time scheduling; recurrent neural network (RNN); utility maximization.

Indexed keywords

CONSTRAINED OPTIMIZATION; DISTRIBUTED COMPUTER SYSTEMS; MATHEMATICAL TRANSFORMATIONS; NETWORKS (CIRCUITS); OPTIMIZATION; PROBLEM SOLVING; SCHEDULING;

APPROXIMATE SOLUTION; APPROXIMATION RATIOS; NEURAL NETWORK MODEL; OPTIMIZATION PROBLEMS; REAL - TIME SCHEDULING; REAL TIME REQUIREMENT; SCHEDULING STRATEGIES; VECTORIZATION TECHNIQUES;

PIECEWISE LINEAR TECHNIQUES;

ARTIFICIAL NEURAL NETWORK; COMPUTER SYSTEM; HUMAN; STATISTICAL MODEL;

COMPUTER SYSTEMS; HUMANS; LINEAR MODELS; NEURAL NETWORKS (COMPUTER);

EID: 84940786221     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2015.2466612     Document Type: Article

References (40)

1. J. W. S. W. Liu, Real-Time Systems. Upper Saddle River, NJ, USA: Prentice-Hall, 2000.
2. G. Buttazzo, Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, vol. 24. New York, NY, USA: Springer-Verlag, 2011.
3. U. Devi, "Soft real-Time scheduling on multiprocessors, " Ph.D. dissertation, Dept. Comput. Sci., Univ. North Carolina Chapel Hill, Chapel Hill, NC, USA, 2006.
4. C. L. Liu and J. W. Layland, "Scheduling algorithms for multiprogramming in a hard-real-Time environment, " J. ACM, vol. 20, no. 1, pp. 46-61, 1973.
5. B. Ravindran, E. D. Jensen, and P. Li, "On recent advances in time/utility function real-Time scheduling and resource management, " in Proc. 8th IEEE Int. Symp. Object-Oriented Real-Time Distrib. Comput. (ISORC), May 2005, pp. 55-60.
6. A.-H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia, "Autonomous demand-side management based on game-Theoretic energy consumption scheduling for the future smart grid, " IEEE Trans. Smart Grid, vol. 1, no. 3, pp. 320-331, Dec. 2010.
7. H. Wu, B. Ravindran, E. D. Jensen, and P. Li, "Energy-efficient, utility accrual scheduling under resource constraints for mobile embedded systems, " ACM Trans. Embedded Comput. Syst., vol. 5, no. 3, pp. 513-542, 2006.
8. A. Burns and R. I. Davis. (2013). Mixed-Criticality Systems: A Review. [Online]. Available: http://www-users.cs.york.ac.uk/~burns/review.pdf
9. D. Tank and J. J. Hopfield, "Simple neural optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit, " IEEE Trans. Circuits Syst., vol. 33, no. 5, pp. 533-541, May 1986.
10. S. Zhang and A. G. Constantinides, "Lagrange programming neural networks, " IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., vol. 39, no. 7, pp. 441-452, Jul. 1992.
11. J. Wang, "A deterministic annealing neural network for convex programming, " Neural Netw., vol. 7, no. 4, pp. 629-641, 1994.
12. Y. Xia, "A new neural network for solving linear and quadratic programming problems, " IEEE Trans. Neural Netw., vol. 7, no. 6, pp. 1544-1548, Nov. 1996.
13. M. Forti, P. Nistri, and M. Quincampoix, "Generalized neural network for nonsmooth nonlinear programming problems, " IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 51, no. 9, pp. 1741-1754, Sep. 2004.
14. X. Hu and J. Wang, "Solving pseudomonotone variational inequalities and pseudoconvex optimization problems using the projection neural network, " IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1487-1499, Nov. 2006.
15. W. Bian and X. Xue, "Subgradient-based neural networks for nonsmooth nonconvex optimization problems, " IEEE Trans. Neural Netw., vol. 20, no. 6, pp. 1024-1038, Jun. 2009.
16. Z. Guo, Q. Liu, and J. Wang, "A one-layer recurrent neural network for pseudoconvex optimization subject to linear equality constraints, " IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 1892-1900, Dec. 2011.
17. Z. Zeng and T. Huang, "New passivity analysis of continuous-Time recurrent neural networks with multiple discrete delays, " J. Ind. Manage. Optim., vol. 7, no. 2, pp. 283-289, 2011.
18. L. Cheng, Z.-G. Hou, Y. Lin, M. Tan, W. C. Zhang, and F.-X. Wu, "Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks, " IEEE Trans. Neural Netw., vol. 21, no. 5, pp. 714-726, May 2011.
19. Q. Liu, Z. Guo, and J. Wang, "A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization, " Neural Netw., vol. 26, pp. 99-109, Feb. 2012.
20. C. Guo and Q. Yang, "A neurodynamic optimization method for recovery of compressive sensed signals with globally converged solution approximating to 0 minimization, " IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 7, pp. 1363-1374, Jul. 2015.
21. Q. Liu and J. Wang, "Finite-Time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions, " IEEE Trans. Neural Netw., vol. 22, no. 4, pp. 601-613, Apr. 2011.
22. J. Y.-T. Leung, Ed., Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Boca Raton, FL, USA: CRC Press, 2004.
23. C. Cardeira and Z. Mammeri, "Neural networks for multiprocessor realtime scheduling, " in Proc. IEEE 6th Euromicro Workshop Real-Time Syst., Jun. 1994, pp. 59-64.
24. C. Cardeira and Z. Mammeri, "Neural network versus max-flow algorithms for multiprocessor real-Time scheduling, " in Proc. IEEE 8th Euromicro Workshop Real-Time Syst., Jun. 1996, pp. 175-180.
25. M. P. Silva, C. Cardeira, and Z. Mammeri, "Solving real-Time scheduling problems with Hopfield-Type neural networks, " in Proc. 23rd EUROMICRO Conf. New Frontiers Inf. Technol., Sep. 1997, pp. 671-678.
26. Y.-M. Huang and R.-M. Chen, "Scheduling multiprocessor job with resource and timing constraints using neural networks, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 29, no. 4, pp. 490-502, Aug. 1999.
27. R.-M. Chen and Y.-M. Huang, "Competitive neural network to solve scheduling problems, " Neurocomputing, vol. 37, nos. 1-4, pp. 177-196, 2001.
28. S. K. Baruah, N. K. Cohen, C. G. Plaxton, and D. A. Varvel, "Proportionate progress: A notion of fairness in resource allocation, " Algorithmica, vol. 15, no. 6, pp. 600-625, 1996.
29. S. Vestal, "Preemptive scheduling of multi-criticality systems with varying degrees of execution time assurance, " in Proc. 28th IEEE Real-Time Syst. Symp. (RTSS), Dec. 2007, pp. 239-243.
30. H. Leontyev, "Compositional analysis techniques for multiprocessor soft real-Time scheduling, " Ph.D. dissertation, Dept. Comput. Sci., Univ. North Carolina Chapel Hill, Chapel Hill, NC, USA, 2010.
31. J. P. Erickson, "Managing tardiness bounds and overload in soft real-Time systems, " Ph.D. dissertation, Dept. Comput. Sci., Univ. North Carolina Chapel Hill, Chapel Hill, NC, USA, 2014.
32. S. K. Baruah and Z. Guo, "Mixed-criticality scheduling upon varyingspeed processors, " in Proc. IEEE 34th Real-Time Syst. Symp. (RTSS), Dec. 2013, pp. 68-77.
33. Z. Guo and S. K. Baruah, "Implementing mixed-criticality systems upon a preemptive varying-speed processor, " Leibniz Trans. Embedded Syst., vol. 1, no. 2, pp. 3:1-3:19, 2014.
34. H. Kellerer, U. Pferschy, and D. Pisinger, Knapsack Problems. Berlin, Germany: Springer-Verlag, 2004.
35. G. C. Buttazzo and J. A. Stankovic, "RED: Robust earliest deadline scheduling, " in Proc. 3rd Int. Workshop Responsive Comput. Syst., 1993, pp. 100-111.
36. S. Baruah et al., "On the competitiveness of on-line real-Time task scheduling, " in Proc. 12th IEEE Real-Time Syst. Symp. (RTSS), Dec. 1991, pp. 106-115.
37. G. Koren and D. Shasha, "Dover : An optimal on-line scheduling algorithm for overloaded uniprocessor real-Time systems, " SIAM J. Comput., vol. 24, no. 2, pp. 318-339, Apr. 1995.
38. B. Kalyanasundaram and K. Pruhs, "Speed is as powerful as clairvoyance, " J. ACM, vol. 47, no. 4, pp. 617-643, 2000.
39. D. Maxim, O. Buffet, L. Santinelli, L. Cucu-Grosjean, and R. I. Davis, "Optimal priority assignment algorithms for probabilistic real-Time systems, " in Proc. 19th Int. Conf. Real-Time Netw. Syst. (RTNS), 2011, pp. 129-138.
40. Z. Guo, L. Santinalli, and K. Yang, "EDF schedulability analysis on mixed-criticality systems with permitted failure probability, " in Proc. 21st IEEE Int. Conf. Embedded Real-Time Comput. Syst. Appl. (RTCSA), Aug. 2015, pp. 187-196.