Abstract
Service-oriented computing (SOC) is a popular software paradigm that is widely employed in IT industry. SOC uses “services” as the unit of functionality of a software application. The massive wave of SOC applications involves considerable energy consumption of servers, which should not be ignored in large-scale computing environment. When a service requirement can be answered by several web services, the energy consumption for each service to reply to the service request may be different. When this happens, web service selection (WSS) is often required to choose appropriate services to maximize global energy efficiency of SOC applications. Accordingly, this paper proposes a Virtual Power Meter Supported Power Consumption Prediction method for WSS (VPMSPCP). VPMSPCP facilitates choosing appropriate services to minimize wasteful electrical energy from the overall environment of SOC applications. According to our empirical proof, there is a correlation between the power consumption of a service and the status of the server where this service resides. We take advantage of this discovery to develop VPMSPCP by combining a ridge regression model with a well-known web service power modeling method. There are mainly two steps to establish VPMSPCP. First, we develop a virtual power meter (VPM) for each server. VPM is used to estimate the average power of a server under a certain status. Second, we apply the VPM to develop VPMSPCP which estimates power consumption of a web service according to the current status of the corresponding servers. Experiments show that VPMSPCP performs well in improving energy saving in WSS.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Verma K, Sheth AP (2007) Semantically annotating a web service. IEEE Internet Comput 11(2):83
Zheng Z, Lyu MR (2013) QoS management of web services. Springer, New York
Lomotey RK, Deters R (2012) Using a cloud-centric middleware to enable mobile hosting of web services. Procedia Comput Sci 10:634–641
Li Q, Dou R, Chen F, Nan G (2014) A QoS-oriented Web service composition approach based on multi-population genetic algorithm for Internet of things. Int J Comput Intell Syst 7(2):26–34
Ngan LD, Kanagasabai R (2013) Semantic Web service discovery: state-of-the-art and research challenges. Pers Ubiquitous Comput 17(8):1741–1752
Kil H, Nam W, Lee D (2013) Behavioural description based web service composition using abstraction and refinement. Int J Web Grid Serv 9(1):54–81
Zhang Y, Huang H, Yang D, Zhang H, Chao HC, Huang YM (2009) Bring QoS to P2P-based semantic service discovery for the Universal Network. Pers Ubiquitous Comput 13(7):471–477
Ranganathan P (2010) Recipe for efficiency: principles of power-aware computing. Commun ACM 53(4):60–67
Bianchini R, Rajamony R (2004) Power and energy management for server systems. Computer 37(11):68–76
Bartalos P, Blake MB (2012) Green web services: modeling and estimating power consumption of web services. In: IEEE 19th international conference on web services (ICWS2012), p 178–185
Wei Y, Brian Blake M (2010) Service-oriented computing and cloud computing: challenges and opportunities. IEEE Internet Comput 14(6):72–75
Contreras G, Martonosi M. (2005). Power prediction for intel XScale® processors using performance monitoring unit events. In: Proceedings of the 2005 international symposium on low power electronics and design, 2005. ISLPED’05, p 221–226
Lawton G (2007) Powering down the computing infrastructure. Computer 40(2):0016–0019
Li T, John LK (2003) Run-time modeling and estimation of operating system power consumption. ACM SIGMETRICS Perform Eval Rev 31(1):160–171
Bartalos P, Blake MB (2012) Engineering energy-aware web services toward dynamically-green computing. In: Service-oriented computing-ICSOC 2011 workshops, p 87–96
Bartalos P, Blake MB, Remy, S. (2011, December). Green web services: Models for energy-aware web services and applications. In: IEEE international conference on service-oriented computing and applications (SOCA), p 1–8
Zheng Z, Ma H, Lyu MR, King I (2013) Collaborative web service qos prediction via neighborhood integrated matrix factorization. IEEE Trans Serv Comput 6(3):289–299
Mobedpour D, Ding C (2013) User-centered design of a QoS-based web service selection system. SOCA 7(2):117–127
Wang S, Hsu CH, Liang Z, Sun Q, Yang F (2014) Multi-user web service selection based on multi-QoS prediction. Inf Syst Front 16(1):143–152
Bartalos P, Blake MB (2012). Green web services: Modeling and estimating power consumption of web services. In: IEEE 19th international conference on web services (ICWS), p 178–185
Acknowledgments
This work was partially supported by the grants of the National Natural Science Foundation of China (61070013, U1135005, 61572374), the Fundamental Research Funds for the Central Universities (Nos. 2042014kf0272, 2014211020201), Guangxi Key Laboratory of Trusted Software (No. kx201421), the Programme of Introducing Talents of Discipline to Universities (No. B07037), “Hundred Talents Recruitment Program” of Global Experts of Hubei.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, J., Jiang, J., Cui, X. et al. Power consumption prediction of web services for energy-efficient service selection. Pers Ubiquit Comput 19, 1063–1073 (2015). https://doi.org/10.1007/s00779-015-0887-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-015-0887-3