臺灣博碩士論文加值系統

為了分享來自於不同來源的網路資源，網路服務描述語言(WSDL) 在過去被提出。它提供一致性且容易閱讀的介面使得資訊/資料可以在網路上交流。此後，應用服務創造者可以聚合來自於不同來源但介面已知的網路服務，進而組成符合使用者複雜需求的新服務。
本論文將探討當運作中的資訊系統所聚合的網路服務規模及資料量變得愈來愈大時，有那些特性值得探索？ 在此前提下，有些特性將變的顯著，像是面對巨量資料的情境，如何友善地匯集及傳播資訊；如何有效率，有價值的操弄巨量資料集。是故，本研究將探索如何設計基於雲端架構的巨量資訊聚合及散布方法。
此外，影像安全監控領域對於本論文所提出的問題而言，是相當合適的例証。因為對於資料匯集來源而言，同時部署的終端裝置有時會超過數千組；另一方面，對於傳播資訊而言，特定部署在熱點的監控攝影機畫面可能會有大量的人同時進行查看。另外，在安全監控產業裡，已有組織訂定通用的網路服務協定，用來相容來自於不同廠商的裝置。但它卻缺少適用於多機管理的協定，比如在多組錄影裝置之間的抽象化的管理介面及容錯控制機制。因此，本研究也以影像安全監控領域為例，設計合適的網路服務聚合介面，提供統一的存取接口及容錯功能。本論文所提出的網路服務補足了先前研究及現有產業標準的不足之處。

In order to be able to share network resource amount via different sources, the Web Service Description Language (WSDL) was proposed. It provides an easy-to-understand interface for information exchange over Internet. Afterward, the application innovators can mash up different Web Services together to create new service models. This is the origin of Web Service composition.
This dissertation will be discussing which features are worth exploring when the scale of information system who composed from Web Services got too large. Under this topic, several characteristics become significant, such as how to friendly aggregate and spread information as well as how to manipulate large scale data set; More specifically, this study will be exploring the design of cloud-based infrastructure for large scale information collection and distribution.
In addition, the video surveillance field is a quite suitable example for the proposed problem. Since the use of information aggregation, the number of simultaneously deployed terminal devices might exceed thousands of units. On the other hand, for information distribution, the cameras which are deployed in hotspot area might have large number of people watching at the same time. Besides, there are commonly used device interoperability Web Service protocol in surveillance industry, but lacking large scale manage protocol such as management interface abstraction and fault-tolerance control between multi recording devices. Hence, this study also takes the video surveillance application as an example to design an appropriate Web Service aggregation interface for unified access entrance and fault-tolerance functionality. The proposed Web Service complement the shortcomings of previous research and existing industry standards

摘要 I
Abstract II
誌謝 III
Table of Contents IV
List Of FIGures VI
List Of Tables VIII
1 Introduction 9
1.1 Cloud computing overview 9
1.2 Features for Cloud computing 10
1.3 Access Acceleration for cloud computing 11
1.4 Applications for cloud computing 13
1.5 Issues to cloud based surveillance 14
1.6 Objective and goal of this dissertation 15
1.7 The value of this study 16
1.8 Organization of the following chapter 17
2 Related works 18
2.1 Information aggregation 18
2.1.1 Introduce to web service composition 18
2.1.2 The RQSS QoS Model 20
2.1.3 The QoS Model 21
2.1.4 The Relaxable QoS-Based Service Selection Algorithm 24
2.1.5 Experimental Results 35
2.1.6 Summary for web service composition 39
2.2 Information Distribution 40
2.2.1 Introduce to CDN 40
2.2.2 Challenges in design cloud based CDN 41
2.2.3 The design of hybrid cloud based CDN, CCDN 42
2.2.4 Summary 49
2.3 Surveillance application overview 49
2.3.1 The concept of cloud based video recorder 49
2.3.2 Scenario for Cloud Video Recorder System 50
2.3.3 Deployment design 51
2.4 Related works in surveillance fault tolerance 52
2.4.1 Device-oriented approach 55
2.4.2 Task-oriented approach 58
2.5 Introduce to ONVIF protocol 59
3. Method 64
3.1. Proposed system overview 65
3.2. ONVIF directory service extension 68
3.3. ONVIF failover service extension 70
3.4. ONVIF data cache service extension 73
3.5. Deployment suggestion 77
3.5.1. Small-scale deployment 78
3.5.2. Medium-scale deployment 78
3.5.3. Large-scale deployment 79
3.5.4. Ultra-scale deployment 79
4. Evaluation 81
4.1 Fault model discussion 81
4.1.1 NVT anomaly 81
4.1.2 DMS anomaly 81
4.1.3 NVS anomaly 82
4.2 Experiment 83
4.2.1 Experiment 1 - maximum local cache time on NVT 84
4.2.2 Experiment 2 - the influence of DMS service interruption 87
4.2.3 Experiment 3 - video loss duration for NVS failure 88
4.2.4 Experiment 4 - the influence to live streaming video when NVS fails 89
4.3 Design issue discussion 91
4.3.1 Erroneous failure detection issues 91
4.3.2 Fault tolerance for recorded data 92
4.3.3 IaaS integration issues 92
5. Conclusion and Future works 94
References 95

[1] Wang, L., Von Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., & Fu, C. (2010). Cloud computing: a perspective study. New Generation Computing, 28(2), 137-146.
[2] Chia-Feng Lin, Ruey-Kai Sheu, Yue-Shan Chang, & Shyan-Ming Yuan), “A relaxable service selection algorithm for QoS-based web service composition”, Information and Software Technology, Vol. 53, No. 12, 2011 Dec. pp. 1370-1381.
[3] Chia-Feng Lin, Ruey-Shyang Wu, Shyan-Ming Yuan, Ching-Tsorng Tsai,“A web services status monitoring technology for distributed system management in the cloud”, IEEE Int’l Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2010
[4] Chia-Feng Lin, Muh-Chyi Leu, Chih-Wei Chang, Shyan-Ming Yuan,“The study and methods for cloud based CDN”, IEEE Int’l Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2011
[5] Hsiu-Pang Yeh, Yue-Shan Chang, Chia-Feng Lin, Shyan-Ming Yuan, “Accelerating 3-DES performance using GPU”, IEEE Int’l Conf. on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2011
[6] Duy-Phuong Pham, Chia-Feng Lin, Shyan-Ming Yuan, Emery Jou “Database Backed by Cloud Data Store for On-premise Applications”, IEEE Int’l Conf. on High Performance Computing and Communications (HPCC), 2011
[7] Chia-Feng Lin, Shyan-Ming Yuan,”The design and evaluation of GPU based memory database”, IEEE Int’l Conf. on Genetic and Evolutionary Computing (ICGEC), 2011
[8] Akinkuolie, B. B, Chia-Feng Lin, Shyan-Ming Yuan), “A cross-platform mobile learning system using QT SDK Framework”, IEEE Int’l Conf. on Genetic and Evolutionary Computing (ICGEC),2011
[9] Yung-Wei Kao, Chia-Feng Lin, Kuei-An Yang, and Shyan-Ming Yuan, “A Web-based, Offline-able, and Personalized Runtime Environment for executing applications on mobile devices”, Computer Standards & Interfaces, Vol.34, No. 1, Jan. 2012 pp. 212-224.
[10] Yi-Hsing Tasi, Chyi-Yuan Muh, Shyan-Ming Yuan, Chia-Feng Lin, Method for Streaming Service Migration in Distributed Video Storage System, TW Patent No. I 466537, 2014
[11] Chia-Feng Lin, Shyan-Ming Yuan, Muh-Chyi Leu, Ching-Tsorng Tsai, “A framework for scalable cloud video recorder system in surveillance environment”, IEEE Int’l Conf. on Ubiquitous intelligence & computing and 9th international conference on autonomic & trusted computing (UIC/ATC), 2012
[12] Jinghai Rao, Xiaomeng Su, A survey of automated web service composition methods, in: Proceedings of the First International Workshop on Semantic Web Services and Web Process Composition, San Diego, California, USA, July,2004, pp. 43–54.
[13] S. Dustdar, W. Schreiner, A survey on web services composition, International Journal of Web and Grid Services 1 (1) (2005) 1–30.
[14] G. Alonso, F. Casati, H. Kuno, V. Machiraju, Web Services Concepts, Architectures and Applications, Springer-Verlag, Berlin, Heidelberg, 2004.
[15] Ziqian Xu, Patrick martin, Wendy Powley, Farhana Zulkernine, Reputationenhanced QoS-based web service discovery, in: Proceedings of International Conference on Web Services, July, 2007, pp.249–256.
[16] D. Ardagna, B. Pernici, Adaptive service composition in flexible processes, IEEE Transactions on Software Engineering 33 (6) (2007) 369–384.
[17] H. Cao1, X. Feng, Y. Sun1, Z. Zhang, Q. Wu, A service selection model with multiple QoS constraints on the MMKP, in: Proceeding of the IFIP International Conference on Network and Parallel Computing Workshops, September, 2007, pp. 584–589.
[18] P. Plebani, B. Pernici, URBE: web service retrieval based on similarity evaluation, IEEE Transaction on Knowledge and Data Engineering 21 (11) (2009) 1629–1642.
[19] D. Stefan, G. Alessio, D. John, Exploiting metrics for similarity-based semantic web service discovery, in: Proceedings of International Conference on Web Services, July, 2009, pp. 327–334.
[20] R. Para-Hernández, N.J. Dimopoulos, A new heuristic for solving the multichoice multidimensional Knapsack problem, IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 35 (5) (2005) 708–717.
[21] S. Khan, K. F. Li, E. G. Manning and M. M. Akbar, Solving the Knapsack Problem for Adaptive Multimedia Systems, Studia Informatica Universalis, Vol. 2(1), (2002), pp.157-178.
[22] Y.S. Luo, Y. Qi, L.F. Shen, D. Hou, C. Sapa, Y. Chen, An improved heuristic for QoS-aware service composition framework, in: Proceeding of IEEE International Conference on High Performance Computing and Communications, September, 2008, pp. 360–367.
[23] Organization for the Advancement of Structured Information Systems (OSAIS), Web Services Business Process Execution Language (WSBPEL). <http://www.oasis.open.org/committees/tc_home.php?wg_abbrev=wsbpel>.
[24] J. Cardoso and A. Sheth, Semantic e-Workflow Composition, Journal of Intelligent Information Systems, Vol. 21, Issue 3, (2003), pp. 191 – 225.
[25] M. Paolucci, T. Kawamura, T. R. Payne and K. Sycara, Importing the Semantic Web in UDDI, in Proceedings of workshop on Web Services, E-Business and Semantic Web, (2002), pp.225-236.
[26] V. Suraci1, S. Mignanti and A. Aiuto, Context-aware Semantic Service Discovery, in Proceedings of the Third International Conference on Semantics, Knowledge and Grid, (Oct., 2007), pp. 499-502.
[27] T. YU, Y. Zhang and K. J. Lin, Efficient Algorithms for Web Services Selection with End-to-End QoS Constraints, ACM Transactions on the Web, Vol. 1, Issue 1, Article 6, (May 2007).
[28] H. Cao1, X. Feng, Y. Sun1, Z. Zhang, Q. Wu, A Service Selection Model with Multiple QoS
[29] PSIA, Physical Security Interoperability Alliance, http://www.psialliance.org
[30] ONVIF, Open Network Video Interface Forum, http://www.onvif.org/Documents/Specifications.aspx
[31] ONVIF PROFILE S Specifications, http://www.onvif.org/Portals/0/documents/op/ONVIF_Profile_S_Specification_v1-1-1.pdf
[32] ONVIF PROFILE G Specification, http://www.onvif.org/portals/0/documents/specs/ONVIF_Profile_G_Specification_v1-0.pdf
[33] X. Wang, Intelligent multi-camera video surveillance: A review, Pattern Recognition Letters. 34.1 (2013) 3-19.
[34] A. Maharana and G. N. Rathna, Fault-tolerant video on demand in RSerPool architecture, Proc. of 2006 Int’l Conf. on Advanced Computing and Communications (2006).
[35] R. Friedman, L. Baram, and S. Abarbane, Fault-tolerant multi-server video-on-demand service, Proc. of 2003 Int’l Parallel and Distributed Processing Symp. (2003).
[36] V.S. Kushwah, S. K. Goyal, and P. Narwariya, A survey on various fault tolerant approaches for cloud environment during load balancing, International Journal of Computer Networking, Wireless and Mobile Communications. 4.6 (2014) 25-34.
[37] S. Toor, L. Osmani, P. Eerola, O. Kraemer, T. Linden, S. Tarkoma and J. White, A scalable infrastructure for CMS data analysis based on OpenStack Cloud and Gluster file system, Journal of Physics: Conference Series. 513.6 (2014).
[38] S. Brenner, B. Garbers, and R. Kapitza, Adaptive and scalable high availability for infrastructure clouds." Proc. of 4th IFIP WG 6.1 Int’l Conf. on Distributed Applications and Interoperable Systems (2014).
[39] V. Pashkov, A. Shalimov, and R. Smeliansky, Controller failover for SDN enterprise networks, Proc. of 2014 First Int’l Science and Technology Conf. (2014).
[40]Snyology, Surveillance solution for large-scale enterprises, https://www.synology.com/en-global/solution/surveillance_large, 2014.
[41] GVD, M900 series failover server, http://www.gvdigital.com/document/Datasheet/GVD%20Storage%20Server%20datasheet.pdf, 2014
[42] Milestone Systems, XProtect advanced VMS 2014 administrator's manual, https://milestonedownload.blob.core.windows.net/files/XProtect%20ACM%2010c/Manuals/Administrator-Manual-Advanced/MilestoneXProtectAdvancedVMS_Administrators_Manual_en-US.pdf, 2014
[43] J. Dean and S. Ghemawat, MapReduce: simplified data processing on large clusters, Communications of the ACM. 51.1 (2008) 107-113.
[44] A. P. Jost, R. E. Mack and L. D. Vince, Method and apparatus for glitchless failover to redundant stream, US Patent No. 8989006. (2015).
[45] Y. Tsai, The cloud streaming service migration in cloud video storage system, Proc. of 27th Int’l Conf. on Advanced Information Networking and Applications Workshops. (2013) 672-677.
[46] H. Y. Chung, F. S. Mak, G. Levy, W. C. Lam, C. V. Cheong, S. H. Tsang, Video recording failover, US Patent No. 8249413. (2012).
[47] J. Rasmussen, Milestone edge storage with flexible retrieval, Milestone White Paper, https://www.milestonesys.com/files/White%20papers/Milestone_Edge_Storage_with_flexible_retrieval.pdf, 2015
[48]Qnap, Surveillance Station 5.1, https://www.qnap.com/solution/surveillance-station/en
[49] ONVIF WSDL and XML Schemas Specifications, ONVIF Recording Control WSDL , http://www.onvif.org/onvif/ver10/recording.wsdl
[50] Everfocus, EDN 3340 - 3 Megapixel HD Indoor IR Dome Network Camera, http://www.everfocus.com/product.cfm?productid=1672, 2011, (accessed 2016.09)
[51] J.R.C. Patterson, Video encoding settings for H.264 excellence, Technical paper from Lighterra, http://www.lighterra.com/papers/videoencodingh264, 2012 (accessed 2016.09).
[52] Chesapeake Marketing & Midlantic Marketing, LLC., Bit rate study – real evaluation, http://midches.com/images/uploads/default/Bitrate_Study.pdf, 2013 (accessed 2016.09)
[53] A. ShaikhAli, O.F. Rana, R.A. Ali, D.W. Walker, UDDI: an extended registry for web services, in: Proceedings of the Symposium on Applications and the Internet Workshops, January, 2003, pp. 85–89.