Advertisement

Cloud System Simulation Modeling

  • Bernard P. ZeiglerEmail author
  • Hessam S. Sarjoughian
Chapter
Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Abstract

A set of services form a system of services, a set of hardware parts form a system of networked components, and the former and latter together form cloud systems. This chapter shows how simulations for cloud system designs can be succinctly characterized in a DEVS Modeling Environment which supports software–hardware co-design. This enables trade-off analyses among alternative architectural designs that exhibit new kinds of inherent complexity that are impractical to stage in real-world settings. An example uses a voice communication system which exhibits features that are common to numerous cloud systems. Using SOA-compliance, the formulation becomes independent of any specific application and this supports developing simulation models for different domains of interest. The simulation platform also can be used with actual services and adapts itself during run-time using dynamic structure capability (Chap.  14). It can be combined with actual cloud systems which can support evaluating system structure scalability and operational efficiency using timeliness and accuracy attributes. Such an environment makes cloud system simulation an attractive, useful tool for early cloud system co-designs and evaluations.

References

  1. Butler, J. M. (1995). Quantum modeling of distributed object computing. Simulation Digest, 24(2), 20–39.CrossRefGoogle Scholar
  2. DEVS-Suite (2017). DEVS-suite simulator. Retrieved from http://devs-suitesim.sourceforge.net.
  3. Edwards, S., Lavagno, L., Lee, E. A., & Sangiovanni-Vincentelli, A. (1997). Design of embedded systems: Formal models, validation, and synthesis. Proceedings of the IEEE, 85(3), 366–390.CrossRefGoogle Scholar
  4. Hild, D. R., Sarjoughian, H. S., & Zeigler, B. P. (2001). DEVS-DOC: A modeling and simulation environment enabling distributed codesign. IEEE SMC Transactions-Part A, 32(1), 78–92.Google Scholar
  5. Hu, W. (2007). Visual and persistent co-design modeling for network systems. PhD Thesis, School of Computing, Information, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.Google Scholar
  6. Hu, W., & Sarjoughian, H. S. (2007). A co-design modeling approach for computer network systems. In Winter Simulation Conference, December, Washington DC, USA (pp. 685–693).Google Scholar
  7. Kim, S., Sarjoughian, H. S., & Elamvazuthi, V. (2009). DEVS-suite: A simulator for visual experimentation and behavior monitoring. In High Performance Computing & Simulation Symposium, Proceedings of the Spring Simulation Conference, 1–7, March, San Diego, CA, USA.Google Scholar
  8. Muqsith, M. (2011). Composing hybrid discrete event system and cellular automata models. PhD Dissertation, Computer Science and Engineering, Arizona State University, Tempe, AZ.Google Scholar
  9. Muqsith, M. A., & Sarjoughian, H. S. (2010). A simulator for service-based software system co-design. In 3rd International ICST Conference on Simulation Tools and Techniques, SIMUTools, 1–9, March, Torremolinos, Malaga, Spain.Google Scholar
  10. Sarjoughian, H. S., & Elamvazhuthi, V. (2009). CoSMoS: A visual environment for component-based modeling, experimental design, and simulation. In 2nd international ICST conference on simulation tools and techniques, SIMUTools, 1–9 March, Rome, Italy.Google Scholar
  11. Sarjoughian, H. S., Kim, S., Ramaswamy, M., & Yau, S. S. (2008). A simulation framework for service-oriented computing systems. In Proceedings of the Winter Simulation Conference, December, Miami, FL, USA (pp. 845–853).Google Scholar
  12. Sarjoughian, H. S., Muqsith, M., Huang, D., & Yau, S. (2012). Validation of service oriented computing DEVS simulation models. In Theory of Modeling and Simulation Symposium, SpringSim Multi-conference, April, Orlando, FL.Google Scholar
  13. Wolf, W. H. (1994). Hardware software co-design of embedded systems. Proceedings of the IEEE, 82(7), 969–989.Google Scholar
  14. Yau, S. S., Ye, N., Sarjoughian, H. S., Huang, D., Roontiva, A., Baydogan, M., et al. (2009). Towards development of adaptive service-based software systems. IEEE Transactions on Services Computing, 2(3), 247–260.CrossRefGoogle Scholar
  15. Yau, S. S., Yin, Y., & An, H. G. (2011). An adaptive approach to optimizing trade-off between service performance and security in service-based systems. International Journal of Web Service Research, 8(2), 74–91.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of ArizonaTucsonUSA
  2. 2.Faculty of Computer Science and Computer Systems EngineeringArizona State University, School of Computing, Informatics, and Decision Systems EngineeringTempeUSA

Personalised recommendations