Abstract
This chapter presents a methodology for conducting a Design Space Exploration (DSE) for Distributed Multi-Processor Embedded systems (DMPE). We introduce the notion of a Q-node to include quality-scaled tasks in the application model. A fuzzy rule-based requirements elicitation framework allows the user to visualize and express the availability requirements in a flexible manner. We employ Cuckoo Search (CS), a metaheuristic that mimics the cuckoo birds’ breeding behavior, to explore the multi-objective design space. A fuzzy engine blends together multiple system objectives viz. Performance, Qualitative Availability and Cost-Effectiveness to calculate the overall fitness function. Experimental results illustrate the efficacy of the DSE tool in yielding high quality architectures in shorter run times and with lesser parameter tuning as compared with genetic algorithm. The fuzzy rules approach for fitness evaluation yields solutions with 24 % higher availability and 14 % higher performance as compared with a conventional approach using prefixed weights.
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References
Mentor Graphics, Vista a complete TLM 2.0-based solution (2011), Available: http://www.mentor.com/esl/vista/overview. Accessed 2 June 2011
Cadence, A cadence vision: EDA360 (2011), Available: http://www.cadence.com/eda360/pages/default.aspx. Accessed 2 June 2011
Synopsys, Platform architect: SoC architecture performance analysis and optimization (2011), Available: http://www.synopsys.com/Systems/ArchitectureDesign/pages/PlatformArchitect.aspx. (Accessed 3 June 2011)
Simulink, mathworks. (Online)
R. Luus, Optimization of system reliability by a new nonlinear integerprogramming procedure. IEEE Trans. Reliab. 24(1), 14–16 (1975)
D. Fyffe, W. Hines, N. Lee, System reliability allocation and a computational algorithm. IEEE Trans. Reliab. 17(2), 64–69 (1968)
Y. Nakagawa, S. Miyazaki, Surrogate constraints algorithm for reliability optimization problems with two constraints. IEEE Trans. Reliab. 3(2), 175–180 (1981)
K. Misra, U. Sharma, An efficient algorithm to solve integer programming problems arising in system-reliability design. IEEE Trans. Reliab. 40(1), 81–91 (1991)
C. Sung, C.Y. Kwon, Branch-and-bound redundancy optimization for a series system with multiple-choice constraints. IEEE Trans. Relaib. 48(2), 108–117 (1999)
B. Suman, Simulated annealing-based multi-objective algorithm and their application for system reliability. Eng. Optim. 35(4), 391–416 (2003)
V. Ravi, B. Murty, P. Reddy, Nonequilibrium simulated annealing algorithm applied to reliability optimization of complex systems. IEEE Trans. Reliab. 46(2), 233–239 (1997)
S. Kulturel-Konak, D. Coit, A.E. Smith, Efficiently solving the redundancy allocation problem using tabu search. IIE Trans. 35(6), 515–526 (2003)
Y.-C. Liang, A. Smith, An Ant System Approach to Redundancy Allocation, in Proceedings of the 1999 Congress on Evolutionary Computation (Washington, D.C., 1999)
Y.-C. Liang, A. Smith, Ant colony paradigm for reliable systems design, in Reliability Engineering, vol. 53, ed. by Computational Intelligence (Springer, Berlin, 2007), pp. 417–423
G. Levitin, X. Hu, Y.-S. Dai, Particle swarm optimization in reliability engineering, in Computational Intelligence in Reliability Engineering, vol. 40, ed. by G. Levitin (Springer, Berlin, 2007), pp. 83–112
P. Yin, S. Yu, W.P.P. Wang, Y.T. Wang, Task allocation for maximizing reliability of a distributed system using hybrid particle swarm optimization. J. Syst. Softw. 80(5), 724–735 (2007)
P. Busacca, M. Marseguerra, E. Zio, Multiobjective optimization by genetic algorithms: application to safety systems. Reliab. Eng. Syst. Safety 72(1), 59–74 (2001)
A. Konak, D.W. Coit, A.E. Smith, Engineering & system safety multi-objective genetic algorithms: a tutorial. Reliab. Eng. Syst. Safety, 992–1007 (2006)
L. Sahoo, A.K. Bhunia, P.K. Kapur, Genetic algorithm based multi-objective reliability optimization in interval environment. Comput. Ind. Eng. 62(1), 152–160 (2012)
X. Yang, S. Deb, Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optimisation 1(4), 330–343 (2010)
I. Olkin, L. Glesser, C. Derman, Probability Models and Applications, 2nd edn. (Prentice Hall College Div, NY, 1994)
K. Anil, C. Shampa, A fuzzy based design Exploration scheme for High Availability Heterogeneous Multiprocessor Systems. eMinds: Int. J. Human-Computer Interact 1(4), 1–22 (2008)
N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (The MIT Press, Cambridge, 1996)
J. Anderson, A Survey of Multiobjective Optimization in Engineering Design, Technical Report Department of Mechanical Engineering (Linköping University, Sweden, 2000)
A. Kumar, S. Chakerverty, Design optimization for reliable embedded system using Cuckoo search, in IEEE International Conference on Electronics Computer Technology (ICECT), Kanyakumari, India, (2011)
L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
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Chakraverty, S., Kumar, A. (2014). A Fuzzy Cuckoo-Search Driven Methodology for Design Space Exploration of Distributed Multiprocessor Embedded Systems. In: Khan, M., Saeed, S., Darwish, A., Abraham, A. (eds) Embedded and Real Time System Development: A Software Engineering Perspective. Studies in Computational Intelligence, vol 520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40888-5_5
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DOI: https://doi.org/10.1007/978-3-642-40888-5_5
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