Model-Based Evaluation of Energy Saving Systems

  • Davide Basile
  • Felicita Di Giandomenico
  • Stefania Gnesi
Chapter

Abstract

Nowadays, there is a great attention towards cautious usage of energy sources to be employed in disparate application domains, including critical infrastructures, to save both in financial terms and in environmental impact. This chapter focuses on stochastic model-based as a support to the analysis of energy saving systems, in combination with other non functional properties, such as reliability, safety and availability. We discuss general guidelines to build a model-based framework to analyse critical cyber-physical systems, where effective energy consumption is required, while assuring imposed levels of resilience. Also, an overview of the most commonly employed methodologies and tools for model-based analysis is provided, and extensive literature is indicated as pointers to relevant research activities performed on this attractive topic over the last decades. Finally, in order to corroborate the proposed framework, a case study in the railway domain is proposed. By adopting the Stochastic Activity Networks formalism, the framework is instantiated to analyse effective trade-offs between energy consumption and satisfaction of other dependability related requirements.

Keywords

Energy-saving Reliability Quality models Stochastic analysis 

References

  1. 1.
    Friedler, F.: Process integration, modelling and optimisation for energy saving and pollution reduction. Appl. Therm. Eng. 30(16), 2270–2280 (2010). http://www.sciencedirect.com/science/article/pii/S1359431110001936. In: Selected Papers from the 12th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction
  2. 2.
    Balbo, G.: Introduction to generalized stochastic petri nets. In: Bernardo, M., Hillston, J. (eds.) Formal Methods for Performance Evaluation, LNCS, vol. 4486, pp. 83–131. Springer, Berlin (2007)CrossRefGoogle Scholar
  3. 3.
    Qiu, Q., Wu, Q., Pedram, M.: Dynamic power management of complex systems using generalized stochastic petri nets. In: DAC. pp. 352–356 (2000)Google Scholar
  4. 4.
    Qiu, Q., Wu, Q., Pedram, M.: Stochastic modeling of a power-managed system: construction and optimization. In: Proceedings of the 1999 International Symposium on Low Power Electronics and Design, 1999, San Diego, California, USA, 16–17, Aug 1999. pp. 194–199 (1999)Google Scholar
  5. 5.
    Erbes, T., Shukla, S.K., Kachroo, P.: Stochastic learning feedback hybrid automata for dynamic power management in embedded systems. In: SMCia/05, IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications, June 2005 (2005)Google Scholar
  6. 6.
    Muller, S.C., Hager, U., Rehtanz, C., Wedde, H.F.: Application of self-organizing systems in power systems control. In: Dieste, O., Jedlitschka, A., Juzgado, N.J. (eds.) PROFES 2012 Proceedings LNCS, vol. 7343, pp. 320–334. Springer, Berlin (2012)Google Scholar
  7. 7.
    Ghasemieh, H., Boudewijn, R. Haverkort, M.R.J., Remke, A.: Energy resilience modeling for smart houses. In: DSN (2015) (to appear)Google Scholar
  8. 8.
    David, R., Alla, H.: On hybrid petri nets. Discrete Event Dyn. Syst. 11(1–2), 9–40Google Scholar
  9. 9.
    Zhu, D., Melhem, R., Mosse, D.: The effects of energy management on reliability in real-time embedded systems. In: IEEE/ACM International Conference on Computer Aided Design, 2004, ICCAD-2004, pp. 35–40, Nov 2004 (2004)Google Scholar
  10. 10.
    Misra, S., Krishna, P.V., Saritha, V., Obaidat, M.S.: Learning automata as a utility for power management in smart grids. IEEE Commun. Mag. 51(1), 98–104 (2013)CrossRefGoogle Scholar
  11. 11.
    Cau sevic, A., Seceleanu, C., Pettersson, P.: Distributed energy management case study: a formal approach to analyzing utility functions. In: Margaria, T., Steffen, B. (eds.) Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications, Lecture Notes in Computer Science, vol. 8803, pp. 74–87. Springer, Berlin (2014)Google Scholar
  12. 12.
    Avizienis, A., Laprie, J.C., Randell, B., Landwehr, C.: Basic concepts and taxonomy of dependable and secure computing. Dependable Secure Comput. IEEE Trans. 1(1), 11–33 (2004)CrossRefGoogle Scholar
  13. 13.
    Front matter: In: Karlin, H.M.T. (ed.) An Introduction to Stochastic Modeling (Revised Edition), pp. iii. Academic Press (1994). http://www.sciencedirect.com/science/article/pii/B978012684885450001X
  14. 14.
    Bernardi, S., Merseguer, J., Petriu, D.C.: Model-Driven Dependability Assessment of Software Systems. Springer, Berlin (2013)Google Scholar
  15. 15.
    Diab, H.B.; Zomaya, A.Y.: Dependable Computing Systems: Paradigms, Performance Issues and Applications. Wiley, USA (2005)Google Scholar
  16. 16.
    Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing. ISORC’08, pp. 363–369. IEEE Computer Society, Washington DC (2008). http://dx.doi.org/10.1109/ISORC.2008.25
  17. 17.
    Basile, D., Chiaradonna, S., Di Giandomenico, F., Gnesi, S.: Stochastic model-based evaluation of reliable energy-saving railroad switch heating systems. Tech. Rep., Istituto di Scienze e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa (2016), http://puma.isti.cnr.it/dfdownload.php?ident=/cnr.isti/2016-TR-009
  18. 18.
    Basile, D., Chiaradonna, S., Di Giandomenico, F., Gnesi, S., Mazzanti, F.: Stochastic model based analysis of energy consumption in a railroad switch heating system. In: Software Engineering for Resilient Systems—Proceedings of 7th International Workshop, SERENE 2015, Paris, France, 7–8 Sept 2015. pp. 82–98 (2015)Google Scholar
  19. 19.
    Haverkort, B.R.: Lectures on formal methods and performance analysis (Chap). In: Markovian Models for Performance and Dependability Evaluation, pp. 38–83. Springer, New York (2002). http://dl.acm.org/citation.cfm?id=567305.567307
  20. 20.
    Lyu, M.R. (ed.): Handbook of Software Reliability Engineering. McGraw-Hill Inc, Hightstown (1996)Google Scholar
  21. 21.
    O’Connor, P.P., Kleyner, A.: Practical Reliability Engineering, 5th edn. Wiley Publishing (2012)Google Scholar
  22. 22.
    Trivedi, K.S., Malhotra, M.: Messung, Modellierung und Bewertung von Rechen und Kommunikationssystemen (Chap). In: Reliability and Performability Techniques and Tools: A Survey, pp. 27–48. Springer, Berlin (1993)Google Scholar
  23. 23.
    Reibman, A., Smith, R., Trivedi, K.: Markov and Markov reward model transient analysis: an overview of numerical approaches. Eur. J. Oper. Res. 40(2), 257–267 (1989). http://www.sciencedirect.com/science/article/pii/0377221789903354
  24. 24.
    Peterson, J.L.: Petri nets. ACM Comput. Surv. 9(3), 223–252 (1977)CrossRefMATHGoogle Scholar
  25. 25.
    Bause, F., Kritzinger, P.S.: Stochastic petri nets: an introduction to the theory. SIGMETRICS Perform. Eval. Rev. 26(2), 2–3 (1998)CrossRefGoogle Scholar
  26. 26.
    Boguna, M., Lafuerza, L.F., Toral, R., Serrano, M.A.: Simulating non-markovian stochastic processes. Phys. Rev. E 90, 042108 (2014)CrossRefGoogle Scholar
  27. 27.
    Sahner, R.A., Trivedi, K., Puliafito, A.: Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package. Springer Publishing Company, Incorporated (2012)Google Scholar
  28. 28.
    German, R.: Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets. Wiley, New York (2000)MATHGoogle Scholar
  29. 29.
    Front matter: In: Grabski, F. (ed.) Semi-Markov Processes: Applications in System Reliability and Maintenance, pp. i–ii. Elsevier (2015). http://www.sciencedirect.com/science/article/pii/B9780128005187099889
  30. 30.
    Sanders, W.H., Meyer, J.F.: Stochastic activity networks: formal definitions and concepts. In: Brinksma, E., Hermanns, H., Katoen, J. (eds.) Lectures on Formal Methods and Performance Analysis, First EEF/Euro Summer School on Trends in Computer Science 2000, Revised Lectures. LNCS, vol. 2090, pp. 315–343. Springer, Berlin (2000)Google Scholar
  31. 31.
    Ciardo, G., Muppala, J., Trivedi, K.: Spnp: Stochastic petri net package (1989)Google Scholar
  32. 32.
    Chiola, G., Franceschinis, G., Gaeta, R., Ribaudo, M.: Greatspn 1.7: Graphical editor and analyzer for timed and stochastic petri nets. Perform. Eval. 24, 47–68 (1995)Google Scholar
  33. 33.
    Bucci, G., Carnevali, L., Ridi, L., Vicario, E.: Oris: a tool for modeling, verification and evaluation of real-time systems. STTT 12(5), 391–403 (2010). doi:10.1007/s10009-010-0156-8 CrossRefGoogle Scholar
  34. 34.
    Clark, G., Courtney, T., Daly, D., Deavours, D., Derisavi, S., Doyle, J.M., Sanders, W.H., Webster, P.: The möbius modeling tool. In: Proceedings of the 9th International Workshop on Petri Nets and Performance Models. pp. 241–250 (2001)Google Scholar
  35. 35.
    http://www.railsco.com/~Eelectric\_switch\_heater\_controls.htmGoogle Scholar
  36. 36.
    Brodowski, D., Komosa, K.: A railroad switch and a method of melting snow and ice in railroad switches (2013). https://data.epo.org/publication-server/rest/v1.0/publication-dates/20131225/patents/EP2677079NWA1/document.html
  37. 37.
    Cannon, J.R.: The One-Dimensional Heat Equation (Cambridge Books Online). Cambridge University Press (1984)Google Scholar
  38. 38.
    Trivedi, K.S.: Probability and Statistics with Reliability, Queuing and Computer Science Applications. Wiley, NY (2008)Google Scholar
  39. 39.
    Antsaklis, P.: Goals and challenges in cyber-physical systems research editorial of the editor in chief. IEEE Trans. Autom. Control 59(12), 3117–3119 (2014)CrossRefGoogle Scholar
  40. 40.
    Banerjee, A., Venkatasubramanian, K.K., Mukherjee, T., Gupta, S.K.S.: Ensuring safety, security, and sustainability of mission-critical cyber-physical systems. Proc. IEEE 100(1), 283–299 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Davide Basile
    • 1
  • Felicita Di Giandomenico
    • 1
  • Stefania Gnesi
    • 1
  1. 1.National Research Council (CNR), Institute of Information Science and Technologies (ISTI)PisaItaly

Personalised recommendations