In large-scale distributed defense modeling and simulation, Data Distribution
Management (DDM) controls and limits the data exchanged reducing the processing
requirements of federates. In this paper, we present a comparative study of a
recently proposed DDM algorithm, called P-Pruning algorithm, with three other known
techniques: region-matching, fixed-grid, and dynamic-grid DDM algorithms. By
populating the multicast group, first only on the basis of X-axis information of
routing space, and pruning the non-overlapping subscriber regions within multicast
groups in successive steps, the P-Pruning algorithm avoids the computational
overheads of other algorithms. From the simulation study, we found that the
P-Pruning algorithm is faster than the other three DDM algorithms. The performance
evaluation results also show that the P-Pruning DDM algorithm uses memory at
run-time more efficiently and requires less number of multicast groups as compared
to the three algorithms. We also present the design and implementation of a
memory-efficient, scalable enhancement to the P-Pruning algorithm.
[1] Gupta, P. and Guha, R. K. 2005. “Design, analysis, and
performance evaluation of an efficient algorithm for data distribution management in
high level architecture”, Computer Science Technical Report
CS-TR-05–12, School of Computer Science, University of Central Florida,
Orlando, FL.
2.
[2] Gupta, P. and Guha, R. K. 2006. “Design, analysis, and
performance evaluation of the P-Pruning DDM algorithm”, In preparation for
submission to Simulation Modelling Practice & Theory Journal.
3.
[3]
Burns, R. C.
,
Rees, R. M.
, and
Long, D. D.2001.
“Efficient data distribution in a Web server
farm”,IEEE Internet Computing, Vol. 5, No. 4,
56–65.
4.
[4]
Chen, L.
, and
Choi, H.2001.
“Approximation algorithms for data distribution with load
balancing of Web servers”,
In Proceedings of the 2001 IEEE International Conference on
Cluster Computing, Los Angeles, CA, pp.
274–281.
5.
[5]
Pullen, J. M.
, et al. 2004.
“Using Web services to integrate heterogeneous simulations in a
grid environment”,
In Proceedings of the International Conference on Computational
Science, Krakow, Poland.
6.
[6]
Lee, P.1997.
“Efficient algorithms for data distribution on distributed memory
parallel computers”,IEEE Transactions on Parallel and Distributed Systems, Vol. 8,
No. 8,
825–839.
7.
[7]
Petty, M. D.2002.
“Comparing high level architecture data distribution management
specifications 1.3 and 1516”,Simulation Practice and Theory, Vol. 9, No.
3–5,
95–119.
8.
[8]
Morse, K. L.
, and
Petty, M. D.2001.
“Data distribution management migration from DoD 1.3 to IEEE
1516”,
In Proceedings of the Fifth IEEE International Workshop on
Distributed Simulation and Real-Time Applications, 13–15
Aug., Cincinnati, OH, pp. 58–65.
9.
[9] Van Hook, D. J., Rak, S. J., and Calvin, J. O. 1996.
“Approaches to RTI implementation of HLA data distribution management
services”, 96–14-084, Fifteenth Workshop on Standards for the
Interoperability of Distributed Simulations, September 16–20,
1996.
10.
[10]
Dahmann, J. S.
,
Fujimoto, R. M.
, and
Weatherly, R. M.1998.
“The DoD high level architecture: an update”,
In Proceedings of the 1998 Winter Simulation Conference,
Washington, DC.
11.
[11] Van Hook, D. J. and Calvin, J. O. 1998. “Data distribution
management in RTI 1.3.”, In Proceedings of the 1998 Spring Simulation
Interoperability Workshop, paper no. 98S-SIW-206, March 9–13, Orlando,
FL.
12.
[12]
Guha, R. K.
, and
Bassiouni M.2002.
“Simulation methods and applications”,Simulation Practice and Theory, Vol. 9, No.
3–5,
91–93.
13.
[13]
Pardo-Castellote, G.2003.
“OMG data distribution service: architectural
overview”,IEEE Military Communications Conference, MILCOM, Vol. 1,
242–247.
14.
[14]
Bourkerche, A.
, and
Roy, A.2002. “Dynamic grid-based approach to data
distribution management”, Journal of Parallel and Distributed
Computing, Vol. 62,
366–392.
15.
[15]
Tan, G.
,
Ayani, R.
,
Zhang, Y. S.
, and
Moradi, F.2000.
“Grid-based data management in distributed
simulation”,
In Proceedings of the 33rd Annual Simulation Symposium,
16–20 April, Washington, DC, pp.
7–13.
16.
[16]
Tan, G.
,
Liang, X.
,
Moradi, F.
, and
Taylor, S.2001.
“An agent-based DDM for high level architecture”,
In Proceedings of the 15th Workshop on Parallel and Distributed
Simulation, 15–18 May, Lake Arrowhead, CA,
pp. 75–82.
17.
[17]
Tan, G.
,
Zhang, Y.
, and
Ayani, R.2000.
“A hybrid approach to data distribution
management”,
In Proceedings of the Fourth IEEE International Workshop on
Distributed Simulation and Real-Time Applications, 25–17
August 2000, San Francisco, CA, pp.
55–61.
18.
[18]
Raczy, C.
,
Tran, G.
, and
Yu, J.2005.
“A sort-based DDM matching algorithm for HLA”,ACM Transactions on Modeling and Computer Simulation, Vol. 15,
No. 1,
14–38.
19.
[19]
Boukerche, A.
, and
Dzermajko, C.2004.
“Performance evaluation of data distribution management
strategies”,Concurrency and Computation: Practice and Experience, Vol. 16,
No. 15,
1545–1573.
20.
[20]
Morse, K. L.
, and
Zyda, M.2002.
“Multicast grouping for data distribution
management”,Simulation Practice and Theory, Vol. 9, No.
3–5,
121–141.
21.
[21] Morse, K. L. 2000. “An adaptive, distributed algorithm for
interest management”, Ph.D. Dissertation, Information & Computer
Science Department, University of California, Irvine, CA.
22.
[22] Petty, M. D. 1997. “Computational geometry techniques for
terrain reasoning and data distribution problems in distributed battlefield
simulation”, Ph.D. Dissertation, Computer Science Department, University
of Central Florida, Orlando, FL.
23.
[23] FDK—Federated simulations development kit.
http://www.cc.gatech.edu/computing/pads/fdk.html
24.
[24]
Fujimoto, R.
,
McLean, T.
,
Perumalla, K.
, and
Tacic, I.2000.
“Design of high-performance RTI software”,
In Proceedings of Distributed Simulations and Real-time
Applications (DS-RT), San Francisco, CA.
25.
[25]
Gupta, P.
, and
Guha, R. K.2006.
“Design and implementation of an efficient algorithm for data
distribution management in high level architecture”,4th Symposium on Design, Analysis, and Simulation of Distributed Systems,
Spring Simulation Conference, April 2–6,
2006, Huntsville, AL.
26.
[26]
Koh, J. B.
,
Lee, B. S.
,
Cai, W. T.
, and
Turner, S. J.2000.
“Multicasting fast messages in RTI-Kit”
, In Proceedings of the 2000 Spring Simulation Interoperability
Workshop, paper no. 00S-SIW-039, March 26–31,
Orlando, FL.
27.
[27]
Riley, G.
,
Ammar, M.
,
Fujimoto, R.
,
Park, A.
,
Perumalla, K.
, and
Xu, D.2004.
“A federated approach to distributed network
simulation”,ACM Transactions on Modeling and Computer Simulation, Vol. 14,
No. 2, pp.
116–148.
28.
[28]
Tacic, I.
, and
Fujimoto, R. M.1998.
“Synchronized data distribution management in distributed
simulations”,
In Proceedings of the Twelfth Workshop on Parallel and
Distributed Simulation, 26–29 May, 1998,
Banff, Alberta, Canada, pp.
108–115.
29.
[29]
Wang, J. Z.
, and
Guha, R. K.2001.
“A novel data caching scheme for multimedia
servers”,Simulation Practice and Theory, Vol. 9, No.
3–5,
193–213.
30.
[30]
Ayani, R.
,
Moradi, F.
, and
Tan, G.2000.
“Optimizing cell-size in grid-based DDM”,
In Proceedings of Fourteenth Workshop on Parallel and Distributed
Simulation, 28–31 May, 2000,
Bologna, Italy, pp.
93–100.
