Increasing Energy Saving with Service-based Process Adaptation

Alessandro Miracca, Pierluigi Plebani


The aim to reduce the energy consumption in data centres is usually analyzed in the literature from a facility and hardware standpoint. For instance, innovative cooling systems and less power hungry CPUs have been developed to save as much more energy as possible. The goal of this paper is to move the standpoint to the application level by proposing an approach, driven by a goal-based model and a Complex Event Processing (CEP) engine, that enables the adaptation of the business processes execution. As several adaptation strategies can be available to reduce the energy consumption, the selection of the most suitable adaptation strategy is often the most critical step as it should be done timely and correctly: adaptation has to occur as soon as a critical point is reached (i.e., reactive approach) or, even before it occurs (i.e., proactive approach). Finally, the adaptation actions must also consider the influence on the performance of the system that should not be violated.


  1. Adya, M. and Collopy, F. (1998). How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation. J. of Forecasting, 17(5-6):481-495.
  2. Asnar, Y., Giorgini, P., and Mylopoulos, J. (2011). Goaldriven risk assessment in requirements engineering. Requir. Eng., 16(2):101-116.
  3. Cheng, H., Tan, P.-N., Gao, J., and Scripps, J. (2006). Multistep-Ahead time series prediction. In Proc. of the 10th Pacific-Asia conf on Advances in Knowledge Discovery and Data Mining, PAKDD'06, pages 765- 774, Berlin, Heidelberg. Springer-Verlag.
  4. Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.
  5. Hermosillo, G., Seinturier, L., and Duchien, L. (2010). Using Complex Event Processing for Dynamic Business Process Adaptation. In Proc. of the 7th IEEE 2010 Int'l Conf. on Services Computing, SCC 7810, pages 466-473, Washington, DC, USA. IEEE.
  6. Kephart, J. and Chess, D. (2003). The vision of autonomic computing. Computer, 36(1):41-50.
  7. Leitner, P., Michlmayr, A., Rosenberg, F., and Dustdar, S. (2010). Monitoring, Prediction and Prevention of SLA Violations in Composite Services. 2012 IEEE 19th Int'l Conference on Web Services, pages 369-376.
  8. Luckham, D. C. (2001). The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
  9. Metzger, A., Chi, C.-H., Engel, Y., and Marconi, A. (2012). Research Challenges on Online Service Quality Prediction for Proactive Adaptation. In Software Services and Systems Research - Results and Challenges (SCube), 2012 Workshop on European, pages 51 -57.
  10. Nguyen, H. and Chan, W. (2004). Multiple neural networks for a long term time series forecast. Neural Comput. Appl., 13(1):90-98.
  11. Raab, F., Kohler, W., and Shah, A. (2001). Overview of the TPC Benchmark C: The Order-Entry Benchmark.
  12. Sen, S. (2008). Business Activity Monitoring Based on Action-Ready Dashboards And Response Loop. In Proceedings of the 1st International Workshop on Complex Event Processing for Future Internet.
  13. Wetzstein, B., Zengin, A., Kazhamiakin, R., Marconi, A., Pistore, M., Karastoyanova, D., and Leymann, F. (2012). Preventing KPI Violations in Business Processes based on Decision Tree Learning and Proactive Runtime Adaptation. J. of Sys. Integration, 3(1):3-18.
  14. Zeng, L., Lingenfelder, C., Lei, H., and Chang, H. (2008). Event-Driven Quality of Service Prediction. In Bouguettaya, A., Krueger, I., and Margaria, T., editors, Service-Oriented Computing - ICSOC 2008, volume 5364 of Lecture Notes in Computer Science, pages 147-161. Springer Berlin Heidelberg.

Paper Citation

in Harvard Style

Miracca A. and Plebani P. (2013). Increasing Energy Saving with Service-based Process Adaptation . In Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, ISBN 978-989-8565-55-6, pages 201-209. DOI: 10.5220/0004379802010209

in Bibtex Style

author={Alessandro Miracca and Pierluigi Plebani},
title={Increasing Energy Saving with Service-based Process Adaptation},
booktitle={Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,
TI - Increasing Energy Saving with Service-based Process Adaptation
SN - 978-989-8565-55-6
AU - Miracca A.
AU - Plebani P.
PY - 2013
SP - 201
EP - 209
DO - 10.5220/0004379802010209