The Community for Technology Leaders
RSS Icon
Issue No.04 - Oct.-Dec. (2013 vol.6)
pp: 511-524
George Kousiouris , National Technical University of Athens, Athens
Andreas Menychtas , National Technical University of Athens, Athens
Dimosthenis Kyriazis , National Technical University of Athens, Athens
Kleopatra Konstanteli , National Technical University of Athens, Athens
Spyridon V. Gogouvitis , National Technical University of Athens, Athens
Gregory Katsaros , National Technical University of Athens, Athens
Theodora A. Varvarigou , National Technical University of Athens, Athens
In modern utility computing infrastructures, like grids and clouds, one of the significant actions of a service provider is to predict the resources needed by the services included in its platform in an automated fashion for service provisioning optimization. Furthermore, a variety of software toolkits exist that implement an extended set of algorithms applicable to workload forecasting. However, their automated use as services in the distributed computing paradigm includes a number of design and implementation challenges. In this paper, a decoupled framework is presented, for taking advantage of software like GNU Octave in the process of creating and using prediction models during the service life cycle of a SOI. A performance analysis of the framework is also conducted. In this context, a methodology for creating parametric or gearbox services with multiple modes of operations based on the execution conditions is portrayed and is applied to transform the aforementioned service framework to optimize service performance. A new estimation algorithm is introduced, that creates performance rules of applications as black boxes, through the creation and usage of genetically optimized artificial neural networks. Through this combination, the critical parameters of the networks are decided through an evolutionary iterative process.
Estimation, Artificial neural networks, Software, Quality of service, Biological system modeling, Unified modeling language, Measurement,parametric services, Service-oriented infrastructures, performance estimation, quality of service, performance analysis, workload forecasting, artificial neural networks, genetic algorithms
George Kousiouris, Andreas Menychtas, Dimosthenis Kyriazis, Kleopatra Konstanteli, Spyridon V. Gogouvitis, Gregory Katsaros, Theodora A. Varvarigou, "Parametric Design and Performance Analysis of a Decoupled Service-Oriented Prediction Framework Based on Embedded Numerical Software", IEEE Transactions on Services Computing, vol.6, no. 4, pp. 511-524, Oct.-Dec. 2013, doi:10.1109/TSC.2012.21
[1] T. Erl, Service-Oriented Architecture: Concepts, Technology, and Design. Prentice-Hall, 2005.
[2] M. Armbrust, A. Fox, R. Griffith, D. Anthony, R.K. Joseph, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, "A View of Cloud Computing," Comm. ACM, vol. 53, no. 4, pp. 50-58, Apr. 2010.
[3] National Institute of Standards and Tech nology, "The NIST Definition of Cloud Computing," publications/nistpubs/ 800-145SP800-145.pdf, 2013.
[4] M. Debusmann and A. Keller, "SLA-Driven Management of Distributed Systems Using the Common Information Model," Proc. IFIP/IEEE Eighth Int'l Symp. Integrated Network Management, pp. 563-576, Mar. 2003.
[5] G. Wang, A. Chen, C. Wang, C. Fung, and S. Uczekaj, "Integrated Quality of Service (QoS) Management in Service-Oriented Enterprise Architectures," Proc. Eighth Int'l IEEE Enterprise Distributed Object Computing Conf. (EDOC '04), Sept. 2004.
[6] Z. He, C. Peng, and A. Mok, "A Performance Estimation Tool for Video Applications," Proc. 12th IEEE Real-Time and Embedded Technology and Applications Symp. (RTAS '06), pp. 267-276, 2006.
[7] S. Benkner and G. Engelbrecht, "A Generic QoS Infrastructure for Grid Web Services," Proc. Advanced Int'l Conf. Telecomm. and Int'l Conf. Internet and Web Applications and Services (AICT-ICIW '06), p. 141, 2006.
[8] P. Hasselmeyer, B. Koller, L. Schubert, and P. Wieder, "Towards SLA-Supported Resource Management," Proc. Second Int'l Conf. High Performance Computing and Comm., pp. 743-752, 2006.
[9] S.A. Jarvis, D.P. Spooner, H.N. Keung, J. Cao, S. Saini, and G.R. Nudd, "Performance Prediction and Its Use in Parallel and Distributed Computing Systems," Future Generation Computer Systems, vol. 22, pp. 745-754, Aug. 2006.
[10] O. Florescu, M.d. Hoon, J. Voeten, and H. Corporaal, "Probabilistic Modelling and Evaluation of Soft Real-Time Embedded Systems," Proc. Int'l Conf. Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS '06), 2006.
[11] K. Singh, E. İpek, S.A. McKee, B.R. de Supinski, M. Schulz, and R. Caruana, "Predicting Parallel Application Performance via Machine Learning Approaches: Research Articles," Concurrency Computation: Practice Experience, vol. 19, pp. 2219-2235, Dec. 2007.
[12] M.N. Haines and M.A. Rothenberger, "How a Service-Oriented Architecture May Change the Software Development Process," Comm. ACM, vol. 53, pp. 135-140, Aug. 2010.
[13] H. Koziolek, "Performance Evaluation of Component-Based Software Systems: A Survey," Performance Evaluation, vol. 67, no. 8, pp. 634-658, 2010.
[14] GAMS Support Wiki, "Using GAMS on the Amazon Elastic Compute Cloud," http://support.gams-software.comdoku. php?id=platform:aws , 2013.
[15] F. Bellard, "FFmpeg Multimedia System," http:/www.ffmpeg. org, 2005.
[16] A.J. Ferrer, F. Hernandez, J. Tordsson, E. Elmroth, A. Ali-Eldin, C. Zsigri, R. Sirvent, J. Guitart, R.M. Badia, K. Djemame, W. Ziegler, T. Dimitrakos, S.K. Nair, G. Kousiouris, K. Konstanteli, T. Varvarigou, B. Hudzia, A. Kipp, S. Wesner, M. Corrales, N. Forgo, T. Sharif, and C. Sheridan, "OPTIMIS: A Holistic Approach to Cloud Service Provisioning, Future Generation Computer Systems," Future Generation Computer Systems, vol. 28, no. 1, pp. 66-77, 2012.
[17] T. Cucinotta, F. Checconi, G. Kousiouris, K. Konstanteli, S.V. Gogouvitis, D. Kyriazis, T.A. Varvarigou, A. Mazzetti, Z. Zlatev, J. Papay, M. Boniface, S. Berger, D. Lamp, and T. Voith, "Manuel Stein: Virtualised E-Learning on the IRMOS Real-Time Cloud," Service Oriented Computing and Applications, vol. 6, no. 2, pp. 151-166, 2012.
[18] R. Wolski, N.T. Spring, and J. Hayes, "The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing," Future Generation Computer Systems, vol. 15, nos. 5/6, pp. 757-768, Oct. 1999.
[19] E. Smirnova, C.M. So, and S.M. Watt, "Providing Mathematical Web Services Using Maple in the MONET Architecture," Proc. MONET Workshop, 2004.
[20] A. YarKhan, J. Dongarra, and K. Seymour, "IFIP International Federation for Information Processing," Grid-Based Problem Solving Environments, P.W. Gaffney and J.C.T. Pool, eds., vol. 239, pp. 215-224, Springer, 2007.
[21] D. Petcu, "Between Web and Grid-Based Mathematical Services," Proc. Int'l Multi-Conf. Computing in the Global Information Technology, Aug. 2006.
[22] A.A Zain, K. Hammond, P.W. Trinder, S.A. Linton, H.-W. Loidl, and M. Costanti, "SymGrid-Par: Designing a Framework for Executing Computational Algebra Systems on Computational Grids," Proc. Seventh Int'l Conf. Computational Science (ICCS '07), May 2007.
[23] Interactive Realtime Multimedia Applications on Service Oriented Infrastructures Project, "IRMOS Application Blueprint," blueprint_ v1_0.pdf , Dec. 2009.
[24] GNU Octave,, 2013.
[25] P. Laface, D. Carra, and R. Lo Cigno, "A Performance Model for Multimedia Services Provisioning on Network Interfaces," Proc. Third Int'l Conf. Quality of Service in Multiservice IP Networks, 2005.
[26] R.J Castaldo, M.A. McKay, and V. Tosic, "Exposing GNU Octave Signal Processing Functions as Extensible Markup Language (XML) Web Services," Proc. Canadian Conf. Electrical and Computer Eng., 2006.
[27] Interactive Realtime Multimedia Applications on Service Oriented Infrastructures Project D3.1.2, "IRMOS Overall Architecture," 1_4_NTUA_v1_0.pdf , Feb. 2009.
[28] G. Kousiouris, D. Kyriazis, K. Konstanteli, S. Gogouvitis, G. Katsaros, and T. Varvarigou, "A Service-Oriented Framework for GNU Octave-Based Performance Prediction," Proc. IEEE Int'l Conf. Services Computing, pp. 114-121, 2010.
[29] L. Zhu and X. Liu, "Technical Target Setting in QFD for Web Service Systems Using an Artificial Neural Network," IEEE Trans. Services Computing, vol. 3, no. 4, pp. 338-352, Oct.-Dec. 2010.
[30] J. Holland, Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, 1975.
[31] GAMS Modeling System, http:/, 2013.
[32] MathWorks, "MATLAB Benchmark," http://www.mathworks. com/help/matlab/ref bench.html, 2013.
[33] Standard Performance Evaluation Corporation, "SPEC's Benchmarks and Published Results," benchmarks.html, 2013.
[34] Berkeley View, "Dwarf Mine," , 2013.
[35] R. Nathuji, A. Kansal, and A. Ghaffarkhah, "Q-Clouds: Managing Performance Interference Effects for QoS-Aware Clouds," Proc. Fifth European Conf. Computer Systems (EuroSys '10), pp. 237-250, 2010.
[36] J. Happe, D. Westermann, K. Sachs, and L. Kapova, "Statistical Inference of Software Performance Models for Parametric Performance Completions," Proc. Int'l Conf. Quality of Software Architectures, pp. 20-35, 2010.
[37] S. Areibi, M. Moussa, and H. Abdullah, "A Comparison of Genetic/Memetic Algorithms and Other Heuristic Search Techniques," Proc. Int'l Conf. Artificial Intelligence, 2001.
[38] C.J. Fourie and W.J. Perold, "Comparison of Genetic Algorithms to Other Optimization Techniques for Raising Circuit Yield in Superconducting Digital Circuits," IEEE Trans. Applied Superconductivity, vol. 13, no. 2, pp. 511-514, June 2003.
[39] A. D'Ambrogio and P. Bocciarelli, "A Model-Driven Approach to Describe and Predict the Performance of Composite Services," Proc. Sixth Int'l Workshop Software and Performance (WOSP '07), 2007.
[40] P. Bocciarelli and A. D'Ambrogio, "Model-Driven Performability Analysis of Composite Web Services," Proc. SPEC Int'l Workshop Performance Evaluation, pp. 228-246, 2008.
[41] P. Bocciarelli and A. D'Ambrogio, "A Model-Driven Method for Describing and Predicting the Reliability of Composite Services," Software and Systems Modeling, vol. 10, no. 2, pp. 265-280, 2011.
[42] M.F. Arlitt and C.L. Williamson, "Internet Web Servers: Workload Characterization and Performance Implications," IEEE/ACM Trans. Networking, vol. 5, no. 5, pp. 631-645, Oct. 1997.
[43] Z. Liu, N. Niclausse, and C.J. Villanueva, "Traffic Model and Performance Evaluation of Web Servers," Performance Evaluation, vol. 46, nos. 2/3, pp. 77-100, Oct. 2001.
[44] P. Barford and M. Crovella, "Generating Representative Web Workloads for Network and Server Performance Evaluation," SIGMETRICS Performance Evaluation Rev., vol. 26, no. 1, pp. 151-160, June 1998.
[45] S. Colajanni, M. Colajanni, S. Tosi, and F.L. Presti, "Real-Time Models Supporting Resource Management Decisions in Highly Variable Systems," Proc. IEEE 29th Int'l Performance Computing and Comm. Conf. (IPCCC '10), pp. 247-254, Dec. 2010.
[46] S. Casolari, M. Colajanni, and S. Tosi, "Detecting Behavioral Variations in System Resources of Large Data Centers," Proc. IEEE 11th Int'l Conf. Computer and Information Technology (CIT '11), pp. 371-378, Aug./Sept. 2011.
[47] L. Costa, "Modeling Interactive Real-Time Applications on Service Oriented Infrastructures," Proc. Third EU ICT IRMOS Public Seminar, Seminar_Modelling-PartI.pdf , 2013.
44 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool