36 7 Vol. 36, No. 7 2010 7 ACTA AUTOMATICA SINICA July, 2010 1, 2 1, 2, 3 1, 2,,,,,,, DOI,,, 10.3724/SP.J.1004.2010.00923 Distributed Simulation System Hierarchical Design Model Based on Quotient Space Granular Computation CHEN Jie 1, 2 WU Di 1, 2, 3 ZHANG Juan 1, 2 Abstract To answer the problem of ambiguous design levels for large-scale distributed simulation systems, this paper proposes a hierarchical system model based on the quotient space theory. This model consists of a system global design level, an architecture level, and a execution structure level to solve the problem of diverse levels on system design. Furthermore, we deeply analyze the relationship between the granules of different levels in both horizontal and vertical directions, and integratedly consider simulation tasks, resources and services. Finally, the model is verified based on a real simulation system design example, and the results illustrate that the flexibility and reusability of the system can be improved with the application of the hierarchical design model. Key words Hierarchical system design model, quotient space, granular computation, distributed simulation system,,,,,, 2009-09-11 2010-01-27 Manuscript received September 11, 2009; accepted January 27, 2010 (973 ) (97361361), (60925011) Supported by National Basic Research Program of China (973 Program) (97361361) and National Science Fund For Distinguished Young Scholars (60925011) 1. 100081 2. 100081 3. VALORIA 56017 1. Department of Automation, Beijing Institute of Technology, 100081, Beijing, P. R. China 2. Education Ministry Key Laboratory of Complex System Intelligent Control and Decision, Beijing Institute of Technology, 100081, Beijing, P. R. China. 3. VALORIA Laboratory, Department of Computer Science and Technology, University of South Brittany, Vannes Cedex 56017, France, DIS (Distributed interactire simulation) [1 2] HLA (High level architecture) [3 5],,,,,,, [6 9],,,
924 36 1,,.,, : 1),,,,, 2),,,,, 3),,,,,,,,,, [6].,,, ( ),, [9], : 1), 2),,,,, 3),,, 2.,, [10].,,,,,,,,, [11]. 2.1,, [12],, ;, ; Fig. 1 1 Granular hierarchical model of distributed simulation system
7 : 925, 1,.,, 2.1.1. : ; ;, [A] [R] [S],,,,, ( ), ( ), DIS HLA, ( ), {A 1, A 2,, A m }, {R 1, R 2,, R n }, {S 1, S 2,, S k },,,,, {a 1, a 2, a 3, }, {r 1, r 2, r 3, }, {s 1, s 2, s 3, } 2.1.2 [9], (X, f, T ), X, f ( ), T, X (X, f, T ), X X, X, [X], (X, f, T ), ([X], [f], [T ]),,,,, P = A, R, S, T, C (1), A ; R ; S ; T ; C, : C a :, ; C r :,, ; C s :, ; C t :,,,, (1),,,,,, : [A] = { } = {} = {{ }{ }{ }}, : [R] = { } = { 1 2} = {{ 1 2 }{ 1}} [S] = {RTI } = { } = {{ }{ }{ }} ;, :,,,,, 2.2.
926 36, 2.2.1,,, ( ). [9]. [A] = A 1, A 2,, A }{{ m = } {{a 11,, a 1i },{a 21,, a 2j },, {a m1,, a ml }}= a 1, a 2, a 3, (2), : [R] = R 1, R 2,, R }{{ m = } {{r 11,, r 1i },{r 21,,r 2j },,{r n1,,r nl }}= r 1, r 2, r 3, (3) [S] = S 1, S 2,, S }{{ m = } {{s 11,, s 1i }, {s 21,, s 2j },,{s k1,,s kl }}= s 1, s 2, s 3, (4),,., X {A, R, S}. [X] = X 1 X 2 = x 1 x 2 (5) 2.2.2, 1),,, A i, A j, (6) P (A i ) Q(A j ) (6), A i A j.,, A i A j P (A i ) = Q(A j ) (7),, R t (A) = [A i (t 1 ), A j (t 2 ), A k (t 3 ), ] (8), A i = R m [A j ] (9) 2),,,, A 1 R 1 (t) A 1 R 2 (t) A 1 R n (t) A AR(t) = 2 R 1 (t) A 2 R 2 (t) A 2 R n (t)...... A m R 1 (t) A m R 2 (t) A m R n (t), t, A i R j (t) i j, A i R j (t) {0, 1}.
7 : 927,, R 1 S 1 (t) R 1 S 2 (t) R 1 S k (t) R RS(t) = 2 S 1 (t) R 2 S 2 (t) R 2 S k (t)...... R n S 1 (t) R n S 2 (t) R n S k (t), R i S j (t) R i S j t,, 1 i n, 1 j k., 2.1,,,,, A 2 A 1, A 1 A 2, : T 1end T 2end (10) T 1end = max(t), A 1 R i (t) = 1 T 2start = min(t), A 2 R i (t) = 1, R 1 A 1, : t > 0, A 1 R 1 (t) 1 (11) S p S q, if R i S p (t 0 ) > 0, then t 0 t=1 R i S q (t) > 0 (12),, 3,,,, IEEE DIS HLA DIS [2] DIS,,,,, ; HLA,,,, 3.1, HLA, [A] = {}; [R] = { }; [S] = {HLA }. 3.2,,, ;, ;, : [A] = {A 1 ( ), A 2 ( ), A 3 ( )}; [R] = {R 1 ( 1), R 2 ( 2)}; [S] = {S 1 ( ), S 2 ( ), S 3 ( )}., : 1), : R t (A) = [A1, A2]; 2), : P (R 1 ) = Q(R 2 ), P (R 2 ) = Q(R 1 ); 3), : P (S 1 ) Q(S 2 ); 4),, P (A i R j ) Q(R j S k ).,
928 36, ( 1, 1) (0, 5) ( 1, 1) (6, 14) (13) (0, 11) ( 1, 1) [ ] 0.29 0.45 0.26 (14) 0.30 0.44 0.26, (13), 1, ; (14),,. 3.3,,, 3 10, 2 7, HLA 7 1 3 Table 1 1 Description of the execution level simulation tasks a 1 R 1(A) = [a 1, a 6] a 1,, A 1 a 2 R 2(A) = [a 2, a 4] a 2,, a 3 R 3(A) = [a 3, a 4 a 5] a 3,, a 4 a 4. A 2 a 5 a 5. a 6 a 6. a 7 a 7 A 3 a 8 a 8 a 9 a 9 a 10 R 3(A) = [a 7 a 8 a 9, a 10] a 10, Table 2 2 Description of the execution level simulation resources r 1 R 1 r 2 P (r 1 r 2 r 3) Q(r 4 r 5 r 6 r 7) r 3 r 4 R 2 r 5 P (r 4 r 5 r 6 r 7) Q(r 1 r 2 r 3),,, r 6 r 7
7 : 929 Table 3 3 Description of the execution level simulation services s 1 P (s 1) Q(s 4) s 1, S 1 s 2 P (s 2) Q(s 4) s 2, s 3 P (s 3) Q(s 4) s 3, S 2 s 4 s 4 s 5 P (s 5) Q(s 1) S 3 s 6 P (s 6) Q(s 1) s 7 P (s 6) Q(s 1) s 5, s 6, s 7,,, (15), 1, ; (16), ( 1, 1) ( 1, 1) ( 1, 1) (0, 4) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (1, 3) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (2, 5) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (6, 9) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (10, 12) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (7, 14) ( 1, 1) (0, 2) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (5, 8) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (3, 11) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) (6, 9) ( 1, 1) ( 1, 1) ( 1, 1) ( 1, 1) 0.02 0.06 0.01 0.20 0.09 0.02 0.01 0.02 0.04 0.03 0.15 0.03 0.04 0.01 0.02 0.05 0.04 0.10 0.03 0.02 0.01 0.02 0.04 0.02 0.13 0.04 0.01 0.01 0.02 0.03 0.01 0.16 0.05 0.02 0.01 0.02 0.05 0.02 0.11 0.03 0.03 0.01 0.02 0.04 0.01 0.14 0.03 0.01 0.01 (15) (16),, 4, DIS HLA,,,,,.,,, Aisa-Link, Flavio Oquendo References 1 Jacob S W. DMSO website [Online], available: http://www. dmso.mil, December 12, 2009 2 IEEE Standard for Distributed Interactive Simulation Application Protocols, IEEE Standard 1278, 1998 3 IEEE Standard for Modeling and Simulation (M and S) High Level Architecture (HLA) Framework and Rules, IEEE Standard 1516, 2000 4 IEEE Standard for Modeling and Simulation (M and S) High Level Architecture (HLA) Federate Interface Specification, IEEE Standard 1516.1, 2000 5 IEEE Standard for Modeling and Simulation (M and S) High Level Architecture (HLA) Object Model Template (OMT) Specification, IEEE Standard 1516.2, 2000 6 Miao Duo-Qian, Wang Guo-Yin, Liu Qing, Lin Zao-Yang, Yao Yi-Yu. Granular Computing: Past, Present and Future. Beijing: Science Press, 2007 (,,,, : :, 2007) 7 Zadeh L A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 1997, 90(2): 111 127 8 Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Berlin: Springer, 1991
930 36 9 Zhang Ling, Zhang Bo. Problem Solution Theory and Application Quotient Space Granular Computing Theory and Application. Beijing: Tsinghua University Press, 2007 (, :, 2007) 10 Wu Di, Chen Jie, Flavio O. A formal model-driven engineering approach for composable simulations. Journal of System Simulation, 2009, 21(18): 5608 5613 (,, Flavio O. MDE, 2009, 21(18): 5608 5613) 11 Wu Di, Chen Jie, Flavio O, Zhang Juan. The distributed simulation system optimal design model based on quotient space. Journal of Central South University (Science and Technology), 2009, 40(1): 268 272 (,, Flavio O,, 2009, 40(1): 268 272) 12 Tu Xu-Yan. Control Theory for Large Scale Systems. Beijing: National Defense Industry Press, 1994 ( :, 1994),. E-mail: chenjie@bit.edu.cn (CHEN Jie Ph. D., professor at Beijing Institute of Technology. His research interest covers complicated system multi-object optimization and decision, intelligent control, and constrained nonlinear control.) 2004 E-mail: wudi312858@gmail.com (WU Di Ph. D. candidate at the Institute of Automation, Beijing Institute of Technology. She received her bachelor degree from the Department of Automation, Beijing Institute of Technology in 2004. Her research interest covers large scale distributed simulation system, optimal system design, and formal system development theory. Corresponding author of this paper.), E-mail: zhjuan@bit.edu.cn (ZHANG Juan Ph. D., associate professor at Beijing Institute of Technology. Her research interest covers constrained nonlinear control and distributed simulation.)