On Energy Efficiency of BPM Enactment in the Cloud

  • Olena SkarlatEmail author
  • Philipp Hoenisch
  • Schahram Dustdar
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 256)


Today, a new infrastructure provisioning approach called Cloud Elasticity is evolving, covering three dimensions of elasticity: resource, cost, and quality. Recently, Cloud Elasticity has been utilized for Business Process Enactment in the Cloud as the involved services face highly volatile demand levels. Through treating the three dimensions equally, so-called Elastic (Business) Processes can be achieved, i.e., by leasing and releasing resources on-demand, and customer’s requirements regarding quality and cost can now be met more easily. However, information technology infrastructures are now counted as a problem linked to global warming, and accounting for energy efficiency is an adequate response towards “Green” initiatives. This paper is focused on the fulfillment of the principles of Green Computing and Green Business Process Management on the basis of Cloud Elasticity to support Elastic Processes. We describe an approach for the enactment of energy-efficient Elastic Processes by means of the ViePEP platform.


Energy efficiency Elastic processes Business Process Enactment Cloud Computing 



This paper is supported by TU Wien research funds.


  1. 1.
    Copil, G., Moldovan, D., Truong, H.-L., Dustdar, S.: Multi-level elasticity control of cloud services. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 429–436. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Dustdar, S., Guo, Y., Satzger, B., Truong, H.-L.: Principles of elastic processes. IEEE Internet Comput. 15(5), 66–71 (2011)CrossRefGoogle Scholar
  3. 3.
    Houy, C., Reiter, M., Fettke, P., Loos, P.: Towards green BPM – sustainability and resource efficiency through business process management. In: Muehlen, M., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 501–510. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Baeyens, T.: BPM in the cloud. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 10–16. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Hoenisch, P., Schuller, D., Schulte, S., Hochreiner, C., Dustdar, S.: Optimization of complex elastic processes. IEEE Trans. Serv. Comput. PP(99), 1–8 (2015). IEEE PressCrossRefGoogle Scholar
  6. 6.
    Nowak, A., Leymann, F., Schumm, D.: The differences and commodities between green and conventional business process management. In: 9th IEEE International Conference in Dependable, Autonomic and Secure Computing, pp. 569–576. IEEE Press (2011)Google Scholar
  7. 7.
    Schulte, S., Hoenisch, P., Venugopal, S., Dustdar, S.: Introducing the vienna platform for elastic processes. In: Ghose, A., Zhu, H., Yu, Q., Delis, A., Sheng, Q.Z., Perrin, O., Wang, J., Wang, Y. (eds.) ICSOC 2012. LNCS, vol. 7759, pp. 179–190. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Hoenisch, P., Schulte, S., Dustdar, S.: Workflow scheduling and resource allocation for cloud-based execution of elastic processes. In: 6th International Conference on Service-Oriented Computing and Applications, pp. 1–8. IEEE Press (2013)Google Scholar
  9. 9.
    Marshall, P., Keahey, K., Freeman, T.: Elastic site: using clouds to elastically extend site resources. In: 10th International Conference on Cluster, Cloud and Grid Computing, pp. 43–52. IEEE Press (2010)Google Scholar
  10. 10.
    Ali-Eldin, A., Tordson, J., Elmroth, E.: An adaptive hybrid elasticity controller for cloud infrastructures. In: IEEE Network Operations and Management Symposium, pp. 204–212. IEEE Press (2012)Google Scholar
  11. 11.
    Beloglazov, A., Buyya, R.: Energy efficient allocation of virtual machines in cloud data centers. In: 10th International Conference on Cluster, Cloud and Grid Computing, pp. 577–578. IEEE Press (2010)Google Scholar
  12. 12.
    Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., Pentikousis, K.: Energy-efficient cloud computing. Comput. J. 53(7), 1045–1051 (2010)CrossRefGoogle Scholar
  13. 13.
    Corradi, A., Fanelli, M., Foschini, L.: Increasing cloud power efficiency through consolidation techniques. In: Symposium on Computers and Communications, pp. 129–134. IEEE Press (2011)Google Scholar
  14. 14.
    Younge, A.J., von Laszewski, G., Lizhe, W., Lopez-Alarcon, S., Carithers, W.: Efficient resource management for cloud computing environments. In: International Green Computing Conference, pp. 357–364. IEEE Press (2010)Google Scholar
  15. 15.
    Ye, K., Huang, D., Jiang, X., Chen, H., Wu, S.: Vitrual machine based energy-efficient data center architecture for cloud computing: a performance perspective. In: International Conference on Green Computing and Communications and International Conference on Cyber, Physical and Social Computing, pp. 171–178. IEEE Press (2010)Google Scholar
  16. 16.
    Kliazovich, D., Bouvry, P., Khan, S.U.: GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J. Supercomput. 62, 1263–1283 (2012)CrossRefGoogle Scholar
  17. 17.
    Tu, C.-Y., Kuo, W.-C., Teng, W.-H., Wang, Y.-T., Shiau, S.: A power-aware cloud architecture with smart metering. In: 39th International Conference on Parallel Processing Workshops, pp. 497–503. IEEE Press (2010)Google Scholar
  18. 18.
    Garg, S.K., Yeo, C.S., Buyya, R.: Green cloud framework for improving carbon efficiency of clouds. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 491–502. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Xu, H., Feng, C., Li, B.: Temperature aware workload management in geo-distributed datacenters. In: ACM SIGMETRICS/International Conference on Measurement and Modeling of Computer Systems, pp. 373–374. ACM (2013)Google Scholar
  20. 20.
    Pakbaznia, E., Ghasemazar, M., Pedram, M.: Temperature-aware dynamic resource provisioning in a power-optimized datacenter. In: Design, Automation, and Test in Europe Conference and Exhibition, pp. 124–129. IEEE Press (2010)Google Scholar
  21. 21.
    Kaushik, R.T., Nahrstedt, K.: T*: a data-centric cooling energy costs reduction approach for big data analytics cloud. In: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–11. IEEE Press (2012)Google Scholar
  22. 22.
    Li, S., Wang, S., Abdelzaher, T., Kihl, M., Robertsson, A.: Temperature aware power allocation: an optimization framework and case studies. Sustain. Comput. J. 2, 117–127 (2012)Google Scholar
  23. 23.
    Jakobi, T., Castelli, N., Nolte, A., Stevens, G., Schonau, N.: Towards collaborative green business process management. In: 28th EnviroInfo ICT for Energy Efficiency Conference, pp. 683–690. BIS-Verlag (2014)Google Scholar
  24. 24.
    Nowak, A., Leymann, F., Schumm, D., Wetzstein, B.: An architecture and methodology for a four-phased approach to green business process reengineering. In: Kranzlmüller, D., Toja, A.M. (eds.) ICT-GLOW 2011. LNCS, vol. 6868, pp. 150–164. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  25. 25.
    Nowak, A., Leymann, F., Schleicher, D., Schumm, D., Wagner, S.: Green business process patterns. In: 18th Conference on Pattern Languages of Programs, article no. 6. ACM, New York (2011)Google Scholar
  26. 26.
    Nowak, A., Breitenbücher, U., Leymann, F.: Automating green patterns to compensate CO\(_2\) emissions of cloud-based business processes. In: 8th International Conference on Advance Engineering Computing and Applications in Sciences, pp. 132–139. IARIA (2014)Google Scholar
  27. 27.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Olena Skarlat
    • 1
    Email author
  • Philipp Hoenisch
    • 1
  • Schahram Dustdar
    • 1
  1. 1.Distributed Systems Group, Institute of Information SystemsTU WienViennaAustria

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