The Journal of Supercomputing

, Volume 75, Issue 11, pp 7723–7745 | Cite as

Locality-aware process placement for parallel and distributed simulation in cloud data centers

  • Saad Zaheer
  • Asad Waqar MalikEmail author
  • Anis Ur Rahman
  • Safdar Abbas Khan


Cloud is a multi-tenant paradigm providing resources as a service. With its easily available computing infrastructure, researchers are adopting cloud for experimental purposes. However, using the platform efficiently for parallel and distributed simulations comes with new challenges. One such challenge is that the simulations comprise logical processes executing on distributed nodes, traditionally, organized in a sequential pattern. This placement strategy leads to delays as frequently communicating processes might get placed farther from one another. In this paper, we proposed a framework to facilitate implementation and evaluation of process placement algorithms inside a three-tier cloud data center. Furthermore, we used the framework to test different process placement strategies based on classical clustering techniques, as well as, our proposed efficient locality-aware placement algorithm. Our evaluation results show a performance gain of \(14.5\%\) for the algorithm in comparison with sequential process placement used in practice.


Parallel and distributed simulations Cloud computing Clustering Process migration 



  1. 1.
    Chen T, Zhu Y, Gao X, Kong L, Chen G, Wang Y (2018) Improving resource utilization via virtual machine placement in data center networks. Mob Netw Appl 23(2):227–238CrossRefGoogle Scholar
  2. 2.
    Dai X, Wang JM, Bensaou B (2014) Energy-efficient virtual machine placement in data centers with heterogeneous requirements. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet). IEEE, pp 161–166Google Scholar
  3. 3.
    D’Angelo G (2011) Parallel and distributed simulation from many cores to the public cloud. In: 2011 International Conference on High Performance Computing and Simulation. IEEE, pp 14–23Google Scholar
  4. 4.
    Dong JK, Wang HB, Li YY, Cheng SD (2014) Virtual machine placement optimizing to improve network performance in cloud data centers. J China Univ Posts Telecommun 21(3):62–70CrossRefGoogle Scholar
  5. 5.
    Duong-Ba TH, Nguyen T, Bose B, Tran TT (2018) A dynamic virtual machine placement and migration scheme for data centers. IEEE Trans Serv Comput. CrossRefGoogle Scholar
  6. 6.
    D’Angelo G, Ferretti S, Marzolla M (2019) Fault tolerant adaptive parallel and distributed simulation through functional replication. Simul Model Pract Theory 93:192–207CrossRefGoogle Scholar
  7. 7.
    D’Angelo G, Marzolla M (2014) New trends in parallel and distributed simulation: from many-cores to cloud computing. Simul Model Pract Theory 49:320–335CrossRefGoogle Scholar
  8. 8.
    Eker A, Williams B, Chiu K, Ponomarev D (2019) Controlled asynchronous GVT: accelerating parallel discrete event simulation on many-core clusters. In: 48th International Conference on Parallel Processing (ICPP 2019), pp 5–8Google Scholar
  9. 9.
    Fu X, Zhao Q, Wang J, Zhang L, Qiao L (2018) Energy-aware vm initial placement strategy based on bpso in cloud computing. Sci Program. CrossRefGoogle Scholar
  10. 10.
    Fujimoto RM (2016) Research challenges in parallel and distributed simulation. ACM Trans Model Comput Simul (TOMACS) 26(4):22MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ghribi C, Hadji M, Zeghlache D (2013) Energy efficient VM scheduling for cloud data centers: exact allocation and migration algorithms. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. IEEE, pp 671–678Google Scholar
  12. 12.
    Hassan M, Babiker A, Amien M, Hamad M (2018) SLA management for virtual machine live migration using machine learning with modified kernel and statistical approach. Eng Technol Appl Sci Res 8(1):2459–2463Google Scholar
  13. 13.
    Jagtap D, Abu-Ghazaleh N, Ponomarev D (2012) Optimization of parallel discrete event simulator for multi-core systems. In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium. IEEE, pp 520–531Google Scholar
  14. 14.
    Li Z, Li X, Wang L, Cai W (2014) Hierarchical resource management for enhancing performance of large-scale simulations on data centers. In: Proceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. ACM, pp 187–196Google Scholar
  15. 15.
    Lin W, Liang C, Wang JZ, Buyya R (2014) Bandwidth-aware divisible task scheduling for cloud computing. Softw Pract Exp 44(2):163–174CrossRefGoogle Scholar
  16. 16.
    Liu X, Wang C, Zhou BB, Chen J, Yang T, Zomaya AY (2012) Priority-based consolidation of parallel workloads in the cloud. IEEE Trans Parallel Distrib Syst 24(9):1874–1883CrossRefGoogle Scholar
  17. 17.
    Malik A, Park A, Fujimoto R (2009) Optimistic synchronization of parallel simulations in cloud computing environments. In: 2009 IEEE International Conference on Cloud Computing. IEEE, pp 49–56Google Scholar
  18. 18.
    Malik AW, Mahmood I (2017) Crash me inside the cloud: a fault resilient framework for parallel and discrete event simulation. In: Proceedings of the Summer Simulation Multi-Conference. Society for Computer Simulation International, p 1Google Scholar
  19. 19.
    Park A, Fujimoto RM (2006) Aurora: an approach to high throughput parallel simulation. In: 20th Workshop on Principles of Advanced and Distributed Simulation (PADS’06). IEEE, pp 3–10Google Scholar
  20. 20.
    Ranjbari M, Torkestani JA (2018) A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers. J Parallel Distrib Comput 113:55–62CrossRefGoogle Scholar
  21. 21.
    Taylor SJ (2019) Distributed simulation: state-of-the-art and potential for operational research. Eur J Oper Res 273(1):1–19MathSciNetCrossRefGoogle Scholar
  22. 22.
    Tian W, He M, Guo W, Huang W, Shi X, Shang M, Toosi AN, Buyya R (2018) On minimizing total energy consumption in the scheduling of virtual machine reservations. J Netw Comput Appl 113:64–74CrossRefGoogle Scholar
  23. 23.
    Wang J, Jagtap D, Abu-Ghazaleh N, Ponomarev D (2013) Parallel discrete event simulation for multi-core systems: analysis and optimization. IEEE Trans Parallel Distrib Syst 25(6):1574–1584CrossRefGoogle Scholar
  24. 24.
    Wang K, Zhou X, Li T, Zhao D, Lang M, Raicu I (2014) Optimizing load balancing and data-locality with data-aware scheduling. In: 2014 IEEE International Conference on Big Data (Big Data). IEEE, pp 119–128Google Scholar
  25. 25.
    Wiseman Y, Feitelson DG (2003) Paired gang scheduling. IEEE Trans Parallel Distrib Syst 14(6):581–592CrossRefGoogle Scholar
  26. 26.
    Yao F, Yao Y, Chen H, Li T, Lin M, Zhang X (2019) An efficient virtual machine allocation algorithm for parallel and distributed simulation applications. Concurrency Comput Pract Experience. CrossRefGoogle Scholar
  27. 27.
    Yao F, Yao Y, Chen H, Li T, Lin M, Zhang X (2019) An intelligent scheduling algorithm for complex manufacturing system simulation with frequent synchronizations in a cloud environment. Memet Comput. CrossRefGoogle Scholar
  28. 28.
    Yoginath SB, Perumalla KS (2015) Efficient parallel discrete event simulation on cloud/virtual machine platforms. ACM Trans Model Comput Simul (TOMACS) 26(1):5MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer Science (SEECS)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Department of Information Systems, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

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