Download Full Issue in PDF - Academy Publisher

More documents

Recommendations

Info

1570 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 [9] J.L. Marchetti, D.A. Mellichamp and D.E. Seborg, “Predictive control based on discrete convolution models,” Industrial Engineering Chemistry Process Design Development, vol. 22:3, pp.488–495, July 1983. doi:10.1021/i200022a025. [10] J.M. Maciejowski, Predictive Control: with Constraints, Prentice Hall, October 2000. [11] T.J. Harris, “Assessment of Closed Loop Performance,” Canadian Journal of Chemical Engineering. vol. 67, pp. 856–861, 1989. [12] T.J. Harris, F. Boudreau and J.F. Macgregor, “Performance Assessment of Multivariable Feedback Controller,” Automatica. vol. 32:11, pp. 1505–1518, November 1996. http://dx.doi.org/10.1016/s0005-1098(96)00108-2. [13] B. Huang, S.L. Shah and E.K. Kwok, “Good, bad or optimal Performance assessment of multivariable processes,” Automatica. vol. 33:6, pp. 1175–1183, June 1997. http://dx.doi.org/10.1016/s0005-1098(97)00017-4. [14] B. Huang and S.L. Shah, Performance assessment of control loops: Theory and Applications, Springer, 1999. [15] M. Jelali, “An overview of control performance assessment technology and industrial applications,” Intelligent Control Systems and Signal Processing, vol. 14:5, pp. 441–466, May 2006. http://dx.doi.org/doi:10.1016/j.conengprac.2005.11.005. [16] R. Srinivasan, R. Rengaswamy and R. Miller, “Control Loop Performance Assessment.1. A Qualitative Approach for Stiction Diagnosis,” Industrial & Engineering Chemistry Research. vol. 44:17, pp. 6708–6718, July 2005. [17] R. Srinivasan, R. Rengaswamy and R. Miller, “Control Loop Performance Assessment.2. Hammerstein Model Approach for Stiction Diagnosis,” Industrial & Engineering Chemistry Research. vol. 44:17, pp. 6719– 6728, July 2005. [18] F.W. Xu, K.H. Lee and B. Huang, “Monitoring control performance via structured closed-loop response subject to output variance/covariance upper bound,” Journal of Process Control, vol. 16:9, pp. 971–984, October 2006. http://dx.doi.org/10.1016/j.jprocont.2006.05.003. [19] T.I. Salsbury, “Continuous-time model identification for closed loop control performance assessment,” Control Engineering Practice. vol. 15:1, pp. 109–121, January 2007. http://dx.doi.org/10.1016/j.conengrac.2006.05.001. [20] M. Bauer, L.K. Craig, “Economic assessment of advanced process control-A survey and framework,” Journal of Process Control. vol. 18:1, pp.2–18, January 2008. [21] R.E. Skelton, T. Iwasaki and K.M. Grigoriadis, A Unified Algebraic Approach to Linear Control Design. Taylor&Francis, 1999. [22] D.J. Chmielewski* and A.M. Manthanwar, “On the Tuning of Predictive Controllers: Inverse Optimality and the Minimum Variance Covariance Constrained Control Problem,” Industrial &Engineering Chemistry Research, vol. 43, pp. 7807–7814, October 2004. doi:10.1021/ie030686e. [23] C.T. Chen, Linear System Theory and Design. Oxford University Press, 2009. [24] S.Boyd, L.E. Ghaoui and E. Feron, “Linear Matrix Inequalities in Systems and Control Theory,” Proceedings Allerton Conference on Communication, Control and Computing, pp. 237–246, October 1993. [25] A. Laub, “A Schur method for solving algebraic Riccati equations,” Automatic Control, vol. 24:6, pp. 913–921, December 1979. [26] F.W. Xu, B. Huang and S. Akande, “Performance Assessment of Model Predictive Control for Variability and Constraint Tuning,” Industrial & Engineering Chemistry Research, vol. 46:4, pp. 1208–1219, January 2007.doi:10.1021/ie060786v. [27] B.L.Vladimir, “About the inverse problem of optimal control,” Application Computer Math, vol. 2:2, pp. 90–97, 2003. [28] J. Li, H.O. Wang and S. Niemann, “On the Relations Between LMIs and AREs: Applications to Absolute Stability Criteria, Robustness Analysis and Optimal Control,” Automatica, vol. 32:10, pp. 1361–1379, 1996. Chao Cheng was born in Shanxi, China, 1983. She received the bachelor degree in Beijing Institute of Fashion Technology in 2004. From August 2004 to January 2007, she worked as control engineer in Jiangsu Yangnong Chemical Group Co.,Ltd. She is currently a second grade postgraduate student in Department of Automation in Beijing University of Chemical Technology. Her research interests include MPC and other advanced control. Zhong Zhao was born in Henan, China, 1970. He received bachelor degree in Zhejiang University in 1992 and master degree and Ph.D. from East China University of Science and Technology in 1995 and 1998 respectively. From 1998 to 2000, he worked as a postdoctol in Tsinghua University, from 2000 to 2002 as Senior Engineer in Honeywell Hi-Spec Solutions and Visiting fellow in Max-Planck-Institute. From 2002 to 2004 he employed as a lecturer by University of Saga, Japan. He is currently a professor in the Department of Automation, and deputy director of Institute of Automation in Beijing University of Chemical Technology, also, member of U.S. IEEE and Japan, SICE. His research interests are the advanced control of complex industrial processes, process monitoring, and multiscale process signal analysis. Haixia Li was born in Gansu, China, 1984. She received her bachelor degree and master degree in Information Science and Technology Institute of Beijing University of Chemical Technology in 2007 and 2010 respectively. Her research direction is advanced control of industrial process. © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1571 A Metadata-driven Cloud Computing Application Virtualization Model Yunpeng Xiao 1,2,* 1. Chongqing Engineering Laboratory of Internet and Information Security, Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China 2. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications (BUPT), Beijing, China Email: shineagle2005@hotmail.com Guangxia Xu 1 , Yanbing Liu 1 and Bai Wang 2 1. Chongqing Engineering Laboratory of Internet and Information Security, Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China 2. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications (BUPT), Beijing, China Email: {xugx, liuyb}@cqupt.edu.cn, wangbai@bupt..edu.cn Abstract—In order to meet the requirements of standardization of virtualization in cloud computing platform, improve the flexibility and expansibility of the system and enhance the capability of management-control of the platform, by means of introducing the features of decoupling and semantic of metadata, a Metadata-driven Cloud Computing Application Virtualization Model(MCCAVM) in software level is proposed in the paper based on Turing machine model and Von Neumann computer architecture. The model achieves the complete life cycle management of the capabilities and services. Based on the formal definition, analyzing the hierarchical structure with multi-role and multi-dimensional view, the paper proposes a Metadata-driven Cloud Computing Application Virtualization System(MCCAVS). Taking the production of virtual cloud storage service as example, this paper gives formal analysis of system running and compares with other relating work. The results show that the model presents good reference on the construction of cloud computing application virtualization platform. Index Terms—MCCAVM, metadata, cloud computing, application virtualization, software architecture I. INTRODUCTION The internet is gradually becoming a kind of computing platform in peace with the rapid expansion and popularization of computer communication technology. As a new computing mode, cloud computing describes a mode of increment, use and delivering for a new type of IT services based on internet. It usually means to apply dynamic scalable and virtual resources through internet[1, 2]. Wikipedia defined cloud computing scenario as follow: Users or clients can submit Manuscript received August 20, 2012; revised October 8, 2012; accepted October 14, 2012. Corresponding author: Yunpeng Xiao (xiaoyp@cqupt.edu.cn). a task, such as word processing, to the service provider, without actually possessing the software or hardware[3]. This description shows that a core issue of cloud computing research is how to achieve virtualization and large-scale application scalability and availability in the virtual environment. A broadly understood of virtualization is that computing elements run on the virtual basis. That is a kind of solution to simplify management and optimize resources. The key question highlights platform standardization, improvement of the platform flexibility and dynamic scalability, reduction of the degree of coupling of platform components and other aspects. There are many virtualization technology researches and explorations: Research [4] and [5] put forward virtualization platform architecture through researches based on service-oriented architecture (SOA), [6] and [7] focus on platform flexibility and dynamic capacity expansion, [8, 9, 10] study on virtualization from storage virtualization, virtual device, network virtualization, and other aspects. Application virtualization uncouples the applications from operating systems, provides a virtual operating environment for the applications. In this environment, not only includes the application executable file, but also includes the runtime environment it requires. In essence, application virtualization is abstracted dependent between low-level application systems and hardware. It can solve the problem of version incompatibility, the limitation of terminal capacity, application system hosting mass, realtime deployment of application, disaster recovery and so on. Metadata is descriptive information about the data. It is semantics on the basic concepts, basic relationships and basic constraints of data model. The metadata can solve problems that model layer can not resolve, such as fuzzy semantic of data model, model integration and sharing of information. By using metadata we can translate © 2013 ACADEMY PUBLISHER doi:10.4304/jcp.8.6.1571-1579
Page 1 and 2:
Journal of Computers ISSN 1796-203X
Page 3:
Corn Moisture Measurement using a C
Page 6 and 7:
1378 JOURNAL OF COMPUTERS, VOL. 8,
Page 8 and 9:
Page 10 and 11:
Page 12 and 13:
Page 14 and 15:
Page 16 and 17:
Page 18 and 19:
Page 20 and 21:
Page 22 and 23:
Page 24 and 25:
Page 26 and 27:
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149: 1520 JOURNAL OF COMPUTERS, VOL. 8,
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
Page 258 and 259:
Page 261:
Call for Papers and Special Issues
Page 264:
(Contents Continued from Back Cover
show all

Download Full Issue in PDF - Academy Publisher

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?