Skip to main content
SpringerLink
Log in

MAHA: Migration-based Adaptive Heuristic Algorithm for Large-scale Network Simulations

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The scalable wireless network simulation poses huge computation challenges as the execution time needed to perform the simulation can be prohibitively high. Parallel and distributed simulation (PADS) approaches have been proposed that use huge memory and high processing power of multiple execution units [i.e., logical processes (LPs)] to handle scalable simulations. Each LP is comprised of a set of simulation entities (SEs) that can interact local or remote SEs. However, the remote communication among SEs and synchronization management across LPs are two main issues related to PADS execution of large-scale simulations. A number of migration techniques have been used to mitigate the problem of high-end remote communication. The problem is that most of the existing migration strategies result in higher number of migrations that ultimately lead to higher computation overhead. In this paper, we propose a migration-based adaptive heuristic algorithm (MAHA). Considering the run-time dynamics of the wireless network simulations, MAHA provides dynamic partitioning of the simulation model to achieve better local communication ratio (LCR). In addition, an adaptive academic simulation cloud platform, namely A-SIM-Cumulus cloud, is deployed for scalable simulations. The MAHA is implemented on A-SIM-Cumulus Cloud and simulations are executed multiple times with different configurations and execution environments. The results with optimum LCR show that the proposed algorithm significantly reduces the number of migrations and achieves a good speedup in terms of parallel (i.e., both multi-core and distributed) execution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Benkhelifa, E., Welsh, T., Tawalbeh, L., Jararweh, Y., Basalamah, A.: Energy optimisation for mobile device power consumption: a survey and a unified view of modelling for a comprehensive network simulation. Mob. Netw. Appl. 21(4), 575–588 (2016)

    Article  Google Scholar 

  2. Bahwaireth, K., Benkhelifa, E., Jararweh, Y., Tawalbeh, M.A., et al.: Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications. EURASIP J. Inf. Secur. 2016(1), 15 (2016)

    Article  Google Scholar 

  3. Fujimoto, R.: Parallel and distributed simulation, in: Proceedings of the 2015 Winter Simulation Conference, pp. 45–59 (2015)

  4. Mubarak, M., Carothers, C.D., Ross, R.B., Carns, P.: Enabling parallel simulation of large-scale hpc network systems. IEEE Trans. Parallel Distrib. Syst. 28, 87–100 (2015)

    Article  Google Scholar 

  5. Angelo, G.D.: The simulation model partitioning problem: An adaptive solution based on self-clustering. Simul. Model. Pract. Theory 70, 1–20 (2017)

    Article  Google Scholar 

  6. Fujimoto, R.M.: Parallel and Distributed Simulation Systems, vol. 300. Wiley, New York (2000)

    Google Scholar 

  7. Rhodes, J.D., Upshaw, C.R., Harris, C.B., Meehan, C.M., Walling, D.A., Navrátil, P.A., Beck, A.L., Nagasawa, K., Fares, R.L., Cole, W.J., et al.: Experimental and data collection methods for a large-scale smart grid deployment: methods and first results. Energy 65, 462–471 (2014)

    Article  Google Scholar 

  8. Zehe, D., Knoll, A., Cai, W., Aydt, H.: Semsim cloud service: large-scale urban systems simulation in the Cloud. Simul. Model. Practice Theory 58, 157–171 (2015)

    Article  Google Scholar 

  9. Angelo, G.D., Marzolla, M.: New trends in parallel and distributed simulation: from many-cores to cloud computing. Simul. Model. Practice Theory 49, 320–335 (2014)

    Article  Google Scholar 

  10. Rittinghouse, J.W., Ransome, J.F.: Cloud Computing: Implementation, Management, and Security. CRC Press, Boca Raton (2016)

    Google Scholar 

  11. Zeng, X., Bagrodia, R., Gerla, M.: Glomosim: a library for parallel simulation of large-scale wireless networks, in: ACM SIGSIM Simulation Digest, Vol. 28, IEEE Computer Society, pp. 154–161 (1998)

  12. Angelo, G.D.: Parallel and distributed simulation from many cores to the public Cloud, in: High Performance Computing and Simulation (HPCS), 2011 International Conference on, IEEE, pp. 14–23 (2011)

  13. D’Angelo, G.: Artis: design and implementation of an adaptive middleware for parallel and distributed simulation, in Technical Report, (2005)

  14. Ibrahim, M., Iqbal, M.A., Aleem, M., Islam, M.A.: Sim-cumulus: An academic Cloud for the provisioning of network-simulation-as-a-service (nsaas), IEEE Access (2018)

  15. Boukerche, A., Fabbri, A.: Partitioning parallel simulation of wireless networks, in: Proceedings of the 32nd conference on Winter simulation, Society for Computer Simulation International, pp. 1449–1457 (2000)

  16. Szymanski, B.K., Saifee, A., Sastry, A., Liu, Y., Madnani, K.: Genesis: a system for large-scale parallel network simulation, in: Proceedings of the sixteenth workshop on Parallel and distributed simulation, IEEE Computer Society, pp. 89–96 (2002)

  17. Group, H.W., et al.: Ieee standard for modeling and simulation (m&s) high level architecture (hla)-framework and rules, IEEE Standard 1516–2000 (2000)

  18. Raczy, C., Tan, G., Yu, J.: A sort-based ddm matching algorithm for hla. ACM Trans. Model. Comput. Simul. 15(1), 14–38 (2005)

    Article  Google Scholar 

  19. Kumova, B.İ.: Dynamically adaptive partition-based data distribution management, in: Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation, IEEE Computer Society, pp. 292–300 (2005)

  20. Cai, W., Turner, S.J., Gan, B.P.: Hierarchical federations: an architecture for information hiding, in: Parallel and Distributed Simulation, 2001. Proceedings. 15th Workship on, IEEE, pp. 67–74 (2001)

  21. Boukerche, A., Tropper, C.: A static partitioning and mapping algorithm for conservative parallel simulations, in: ACM SIGSIM Simulation Digest, Vol. 24, ACM, pp. 164–172 (1994)

  22. Boukerche, A., Das, S.K.: Dynamic load balancing strategies for conservative parallel simulations, in: Parallel and Distributed Simulation, 1997., Proceedings., 11th Workshop on, IEEE, pp. 20–28 (1997)

  23. Yocum, K., Eade, E., Degesys, J., Becker, D., Chase, J., Vahdat, A.: Toward scaling network emulation using topology partitioning, in: Modeling, Analysis and Simulation of Computer Telecommunications Systems, 2003. MASCOTS 2003. 11th IEEE/ACM International Symposium on, IEEE, pp. 242–245 (2003)

  24. Vigueras, G., Lozano, M., Orduña, J.M., Grimaldo, F.: A comparative study of partitioning methods for crowd simulations. Appl. Soft Comput. 10(1), 225–235 (2010)

    Article  Google Scholar 

  25. Angelo, G.D., Ferretti, S., Ghini, V.: Distributed hybrid simulation of the internet of things and smart territories, Concurrency and Computation: Practice and Experience

  26. Angelo, G.D., Ferretti, S., Ghini, V.: Modeling the internet of things: a simulation perspective, in: High Performance Computing & Simulation (HPCS), 2017 International Conference on, IEEE, pp. 18–27 (2017)

  27. Ferretti, S., D’Angelo, G., Ghini, V., Marzolla, M.: The quest for scalability and accuracy: Multi-level simulation of the internet of things, arXiv preprint arXiv:1710.02282

  28. Logan, B., Theodoropoulos, G.: The distributed simulation of multiagent systems. Proc. IEEE 89(2), 174–185 (2001)

    Article  Google Scholar 

  29. Peschlow, P., Honecker, T., Martini, P.: A flexible dynamic partitioning algorithm for optimistic distributed simulation, in: Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation, IEEE Computer Society, pp. 219–228 (2007)

  30. Angelo, G.D., Bracuto, M.: Distributed simulation of large-scale and detailed models. Int. J. Simul. Process Model. 5(2), 120–131 (2009)

    Article  Google Scholar 

  31. Bononi, L., Bracuto, M., D’Angelo, G., Donatiello, L.: Performance analysis of a parallel and distributed simulation framework for large scale wireless systems, in: Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, ACM, pp. 52–61 (2004)

  32. Angelo, G.D., Ferretti, S.: Simulation of scale-free networks, in: Proceedings of the 2nd International Conference on Simulation Tools and Techniques, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), p. 20 (2009)

  33. Serrano-Iglesias, S., Gómez-Sánchez, E., Bote-Lorenzo, M.L., Asensio-Pérez, J.I., Rodríguez-Cayetano, M.: A self-scalable distributed network simulation environment based on cloud computing. Clust. Comput. 21(4), 1899–1915 (2018)

    Article  Google Scholar 

  34. D’Angelo, G., Ferretti, S., Marzolla, M., Armaroli, L.: Fault-tolerant adaptive parallel and distributed simulation, in: Distributed Simulation and Real Time Applications (DS-RT), 2016 IEEE/ACM 20th International Symposium on, IEEE, pp. 37–44 (2016)

  35. I. Eucalyptus Systems, “Eucalyptus community cloud,” http://open.eucalyptus.com/try/community-cloud [online] Accessed on

  36. Zhou, A.C., He, B., Ibrahim, S.: “A taxonomy and survey of scientific computing in the cloud,” Big Data: Principles and Paradigms, Morgan Kaufmann, eScience and Big Data Workflows in Clouds

  37. Wainer, G.A., Mosterman, P.J.: Discrete-Event Modeling and Simulation: Theory and Applications. CRC Press, Boca Raton (2016)

    Google Scholar 

  38. Qu, Y., Zhou, X.: Large-scale dynamic transportation network simulation: a space-time-event parallel computing approach. Transp. Res. C 75, 1–16 (2017)

    Article  Google Scholar 

  39. Rawat, P., Singh, K.D., Chaouchi, H., Bonnin, J.M.: Wireless sensor networks: a survey on recent developments and potential synergies. J. Supercomput. 68, 1–48 (2014)

    Article  Google Scholar 

  40. Musolesi, M., Mascolo, C.: Mobility models for systems evaluation, in: Garbinato, B., Miranda, H., Rodrigues, L. (eds.) Middleware for Network Eccentric and Mobile Applications, pp. 43–62. Springer, New York (2009)

  41. Yang, C., Chi, P., Song, X., Lin, T.Y., Li, B.H., Chai, X.: An efficient approach to collaborative simulation of variable structure systems on multi-core machines. Clust. Comput. 19(1), 29–46 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Virtual University of Pakistan, Rawalpindi, 46000, Pakistan

    Muhammad Ibrahim

  2. Department of Computer Science, SWJTU-Leeds Joint School, Southwest Jiaotong University (SWJTU), Chengdu, 611756, People’s Republic of China

    Muhammad Azhar Iqbal

  3. Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan

    Muhammad Aleem & Muhammad Arshad Islam

  4. Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, 700000, Vietnam

    Nguyen-Son Vo

Authors
  1. Muhammad Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Azhar Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Aleem
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Muhammad Arshad Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nguyen-Son Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Azhar Iqbal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ibrahim, M., Iqbal, M.A., Aleem, M. et al. MAHA: Migration-based Adaptive Heuristic Algorithm for Large-scale Network Simulations. Cluster Comput 23, 1251–1266 (2020). https://doi.org/10.1007/s10586-019-02991-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-019-02991-5

Keywords

Search

Navigation