一個以服務品質為基礎的網際服務選擇最佳化方法

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藍啟維(Ci-Wei Lan) 查詢紙本館藏

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論文名稱

一個以服務品質為基礎的網際服務選擇最佳化方法
(An Epsilon-Optimization Approach on QoS-Based Web Services Selection)

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摘要(中)

網際服務為時下最熱門的服務導向技術之一，它能藉由各種簡易及標準化的溝通協定來協助跨平台的應用程式整合，然而其分散式及動態連結的特性也引發了許多不同的研究議題，其中以服務品質為基礎的網際服務選擇即是當前最亟待解決的問題之一。由於網際服務的蓬勃發展，越來越多的服務提供者開始發佈各式各樣的網際服務，而使用者也逐漸獲得更多的選擇空間。但針對一個工作流程內的每項功能，若有眾多符合需求的服務可供選擇時，使用者將面臨到該如何依據整體服務品質的效能，從數以萬計的可能中挑選出最佳服務組合的難題。為了解決上述的問題，我們提出一個以服務品質為基礎的網際服務選擇最佳化方法。首先，根據不同結構樣板的特性來定義相對應的服務品質評估方法，並將工作流程設計解構為不同的樣板組合，進而推導出該工作流程的整體服務品質評估標準。其次，援引近似柏拉圖優勢之定義，使用者可依其對不同屬性的主觀敏感度來衡量不同服務組合間的服務品質優劣。根據量化後的使用者主觀敏感度，可將搜尋空間區分為有限個等價類群，輔以所設計的基因演算法逐步地分析、比較，將可找出符合使用者需求的最佳服務組合。我們所提出的最佳化方法有三個主要的貢獻。(1)使用者可自行依主觀偏好設定每一個服務品質目標函數的等價範圍，而搜尋機制將會根據不同使用者的偏好找出相對應的最佳服務組合。因此，使用者可節省過濾主觀認知上相同服務組合的時間。(2)依據不同等價類群在各個服務品質維度的分佈情形，可推導出該次選擇中之最佳服務組合的數目極值，進而可以在搜尋方法上規劃一個適當大小的儲存空間，以確保不會因儲存空間不足而使曾尋獲之最佳服務組合遺失。(3)可利用曼哈頓準則將搜尋空間轉換為度量空間，藉由評估不同服務組合間的品質差距，將可搜尋出大量在各個服務品質維度都具有較好性能的最佳服務組合。

摘要(英)

While a lot of services providing identical application functionality are available, Quality-of-Service (QoS) becomes a critical concern to distinguish alternatives from each other. QoS-based services selection is the problem of determining optimal combinations among exponential candidates based on multiple QoS objectives. First, a pattern-wise replacement method is designed to derive composite QoS metrics of a given workflow process. Based on XML-encoded process descriptions and aggregative QoS effects of primitive patterns, it is able to obtain the composite QoS metrics by decomposing non-atomic sub-processes repeatedly. Second, the ε-Pareto dominance relations are employed to help discriminate the QoS quality of combinations in terms of user-defined ε values. Thus the optima can be determined by the set of non-ε-Pareto-dominated combinations. Third, the Epsilon-Pareto Genetic Algorithm (EPGA) is designed to find out the optimal combinations by evolutionary computation. In comparison with related work, the proposed approach has several merits including (1) the ε-Pareto dominance relations enable user to specify subjective sensibility of QoS metrics. Equivalents will be filtered off and only those distinguishable combinations are returned. Therefore, user is saved from identifying equal combinations. (2) According to user-defined ε values for different QoS objectives, it is proved that the number of optimal combinations is finite and estimable. By keeping an archive with appropriate size, all found non-ε-Pareto-dominated combinations will never get lost. (3) The whole search space can be transformed into a metric space based on user-defined ε values and the quality between combinations can be measured by the Manhattan norm. By quantitative quality measurement, the proposed approach enables more explorations of optimal combinations that have more prominent performance in every QoS dimension than others.

關鍵字(中)

★ 多目標最佳化
★ 柏拉圖優勢
★ 網際服務
★ 服務品質

關鍵字(英)

★ Multi-objective optimization
★ Pareto dominance
★ Web services
★ QoS

論文目次

Abstract ii
List of Figures v
List of Tables vii
1 Introduction 1
1.1 Web services and service-oriented computing 1
1.2 A reference model of Web services QoS metrics 6
1.3 QoS-based Web services selection problem 11
2 Related work 14
2.1 Web services: The cornerstone of software interoperability 14
2.1.1 SOAP: The standardized message framework 15
2.1.2 WSDL: The standardized service description model 19
2.1.3 UDDI: Web services registry and discovery 24
2.1.4 An application of Web services enhanced e-learning 28
2.2 Multi-objective optimization 31
2.2.1 Pareto dominance based optimization 32
2.2.2 Aggregation based optimization 34
2.3 Related literature on QoS-based Web services selection 37
2.3.1 QoS model and trustworthy service computing 37
2.3.2 Composite QoS metrics 43
2.3.3 QoS-based service discovery and composition 44
3 An ε-optimization approach on QoS-based Web service selection 52
3.1 A pattern based QoS aggregation method 52
3.2 The Epsilon Pareto Genetic Algorithm (EPGA) 60
4 Experiments and Discussions 73
5 Conclusions and Future Work 83
References 85

參考文獻

1 Aalst, W.v.d. (2003). Don’t Go with the Flow: Web Services Composition Standards Exposed, IEEE Intelligent Systems, in S. Staab (eds.) Web Services: Been There, Done That, 18(1), 72-76.
2 Amazon.com Inc. (2006). Amazon Web Services Solutions Catalog. http://solutions. amazonwebservices.com/connect/index.jspa
3 Andrew, T., Curbera, F., Dholakia, H., Goland, Y., Klein, J., Leymann, F., Liu, K., Roller, D., Smith, D., Thatte, S., Trickovic, I. and Weerawarana, S. (2003). Business Process Execution Language for Web Services Version 1.1, http://download.boulder.ibm.com/ibmdl/ pub/software/dw/specs/ws-bpel/ws-bpel.pdf.
4 Andrieux, A., Czajkowski, K., Dan, A., Keahey, K., Ludwig, H., Nakata, T., Pruyne, J., Rofrano, J., Tuecke, S. and Xu, M. (2005). Web Services Agreement Specification (WS-Agreement), http://www.ggf.org/Public_Comment_Docs/Documents/Oct-2005/WS-AgreementSpecificationDraft050920.pdf.
5 Back, T. (1996). Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York.
6 Bajaj, S., Box, D., Chappel,D., Curbera, F., Daniels, G., Hallam-Baker, P., Hondo, M., Kaler, C., Langworthy, D., Nadalin, A., Nagaratnam, N., Prafullchandra, H., Riegen, C.v., Roth, D., Schilimmer, J., Sharp, C., Shewchuk, J., Vedamuthu, A., Yalcinalp, U. and Orchard, D. (2006). Web Services Policy 1.2 – Framework (WS-Policy), http://www.w3.org/Submission/WS-Policy/.
7 Berners-Lee, T., Fielding, R. and Masinter, L. (2005). Uniform Resource Identifier (URI): Generic Syntax. http://www.gbiv.com/protocols/uri/rfc/rfc3986.html
8 Black, P.E. (ed.) (2006). “Manhattan distance” in Dictionary of Algorithms and Data Structures, U.S. National Institute of Standards and Technology. http://www.nist.gov/dads/HTML/ manhattanDistance.html.
9 Bray, T., Hollander, D. and Layman, A. (1999). Namespaces in XML. http://www.w3.org/TR /1999/REC-xml-names-19990114/.
10 Bray, T., Paoli, J., Sperberg-McQueen, C.M. and Maler, E. (2000). Extensible Markup Language (XML) 1.0 (Second Edition). http://www.w3.org/TR/2000/REC-xml-20001006.
11 Cardoso, J., Sheth, A., Miller, J., Arnold, J. and kochut, K. (2004). Quality of Service for Workflows and Web Service Processes, Elsevier Journal of Web Semantics, 1(3), 281-308.
12 Charnes, A. and Cooper, W.W. (1961). Management Models and Industrial Applications of Linear Programming, Vol. 1, New York: John Wiley.
13 Chesbrough, H. and Spohrer, J. (2006). A Research Manifesto for Service Science, Communications of the ACM, 49(7), 35-40.
14 Chinnici, R., Moreau, J.-J., Ryman, A. and Weerawarana, S. (eds) (2007). Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language. http://www.w3.org/ TR/wsdl20/.
15 Clement, L., Hately, A., Riegen, C.v. and Roger, T. (eds) (2004). UDDI Version 3.0.2. http://uddi.org/pubs/uddi_v3.htm.
16 Coello, C.A. (2000). An Updated Survey of GA-Based Multiobjective Optimization Techniques, ACM Computing Surveys, 32(2), 109-143.
17 Coello, C.A., Veldhuizen, D.A.V. and Lamont, G.B. (2002). Evolutionary Algorithms for Solving Multi-Objective Problems. New York: Kluwer Academic/Plenum Publishers.
18 Curbera, F., Duftler, M., Khalaf, R., Nagy, W., Mukhi, N. and Weerawarana, S. (2002). Unraveling the Web services Web: An Introduction to SOAP, WSDL and UDDI, IEEE Internet Computing, 6(2), 86-93.
19 Curbera, F., Khalaf, R., Mukhi, N., Tai, S. and Weerawarana, S. (2003). Communications of the ACM, 46(10), 29-34.
20 Doorn, L.v., Dyer, J., Perez, R. and Sailer, R. (2005). 4758/Linux Project, http://www.research.ibm.com/secure_systems_department/projects/linux4758/index.html
21 Duerst M and Suignard M (2005) Internationalized Resource Identifiers (IRIs). http://www.ietf.org/rfc/rfc3987.txt
22 Erdogmus, H. (2007). On-Demand Enterprise Services: Where’s the Catch, IEEE Software, 24(4), 5-7.
23 Erradi, A. and Maheshwari, P. (2005). wsBus: QoS-aware Middleware for Reliable Web Services Interactions, in Proc. of IEEE Enterprise Computing, e-Commerce and e-Services, 634-639.
24 Evtushenko, Y.G. and Potapov, M.A. (1987). Methods of numerical solution of multi-criterion problem. Soviet mathematics – doklady, 3(4), 420–423.
25 Farina, M. and Amato, P. (2003). Fuzzy Optimality and Evolutionary Multiobjective Optimization, in Proc. of Evolutionary Multi-Criterion Optimization, LNCS Vol. 2632, 58-72.
26 Feldman, S.I., Nathan, K.S., Li, T., Hidaka, K. and Schulze, C. (2006). The Clarion Call for Modern Services: China, Japan, Europe and U.S. Communications of the ACM, 49(7), 86-87.
27 Foster, I. (2004). Grids’ Place in the Service-Oriented Architecture, http://www. techworld.com/opsys/features/index.cfm?featureid=1029.
28 Galileo International, L.L.C. (2006). Galileo Web Services. http://www.galileo.com/ galileo/en-us/agency/Products/GalileoWebServices.htm.
29 Garvin, D.A. (1988). Managing quality: The strategic and competitive edge, New York: Free Press, 49-68.
30 Gass, S. and Saaty, T.L. (1955). The computational algorithm for the parametric objective function. Naval Research Logistics Quarterly, 2, 39-45.
31 Google Inc. (2006). Google SOAP Search API. http://code.google.com/apis/soapsearch/ reference.html.
32 Gudgin, M., Hadley, M., Mendelsohn, N., Moreau, J.-J. and Nielsen, H.F. (eds) (2007). SOAP Version 1.2 Part 1: Messaging Framework. http://www.w3.org/TR/soap12-part1/.
33 Haas, H. and Brown, A. (2004). Web Services Glossary. http://www.w3.org/TR/ws- gloss/.
34 Hill, T.P. (1977). On goods and services. The Review of Income and Wealth, 23(4), 314-339.
35 Holland, J. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor: Michigan.
36 Hwang, C.L. and Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag.
37 IBM. Service Science, Management and Engineering. http://www.research.ibm.com/ ssme/.
38 IDC.com (2006). http://www.idc.com.
39 Ijiri, Y. (1965). Management Goals and Accounting for Control. North-Holland Amsterdan.
40 Jaeger, M.C., Rojec-Goldmann, G. and Muhl, G. (2004). QoS Aggregation for Web Service Composition Using Workflow Patterns, in Proc. of IEEE Enterprise Distributed Object Computing, 149-159.
41 Jaeger, M.C., Rojec-Goldmann, G. and Muhl, G. (2004). QoS Aggregation in Web Service Composition, in Proc. of IEEE Enterprise Computing, E-Commerce and E-Services, 181-185.
42 Jay, F. and Mayer, R. (1990). IEEE Standard Glossary of Software Engineering Terminology, IEEE Std 610.12-1990.
43 Karp, R. (1972). Reducibility Among Combinatorial Problems, In Proc. of a Symposium on the Complexity of Computer Computations, Plenum Press.
44 Kavantzas, N., Burdett, D., Ritzinger, G. and Lafon, Y. (eds.) (2004). Web Services Choreography Description Language Version 1.0. WWW Consortium, http://www.w3.org/ TR/2004/WD-ws-cdl-10-20041217/.
45 Keller, A. and Ludwig, H. (2003). The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services, Plenum Publishing, Journal of Network and Systems Management, 11(1), 57-81.
46 Koppen, M., Franke, K. and Nickolay, B. (2003). Fuzzy-Pareto-Dominance Driven Multiobjective Genetic Algorithm, in Proc. of 10th IFSA World Congress, 450-453.
47 Koppen, M., Vicente-Garcia, R. and Nickolay, B. (2005). Fuzzy-Pareto-Dominance and its Application in Evolutionary Multi-Objective Optimization, in Proc. of Evolutionary Multi-Criterion Optimization, LNCS Vol. 3410, 399-412.
48 Laumanns, M., Thiele, L., Deb, K. and Zitzler, E. (2002). Combining convergence and diversity in evolutionary multi-objective optimization. Evolutionary Computation, 10(3), 1-21.
49 Little, M. (2003). Transactions and Web Services, Communications of the ACM, 46(10), 49-54.
50 Liu, P. and Chen, Z. (2004). An Extended RBAC Model for Web Services in Business Process, in Proc. of IEEE CEC-East, 100 – 107.
51 Liu, Y. A., Ngu, H. H. and Zeng, L. (2004). QoS Computation and Policing in Dynamic Web Service Selection, in Proc. of 13th World Wide Web conference (WWW), 66-73.
52 Ma, K.J. (2005). Web Services: What’s Real and What’s Not, IEEE IT Pro, 7(2), 14-21.
53 Maglio, P.P., Srinivasan, S., Kreulen, J.T. and Spohere, J. (2006). Service Systems, Service Scientists, SSME and Innovation, Communications of the ACM, 49(7), 81-85.
54 McGuinness, D.L. and Harmelen, F.V. (2004). OWL Web Ontology Language Overview, W3C, http://www.w3.org/TR/owl-features/.
55 Menasce, D.A. (2002). QoS Issues in Web Services, IEEE Internet Computing, 6(6), 72-75.
56 Menasce, D.A. (2004). Composing Web Services: A QoS View, IEEE Internet Computing, 8(6), 88-90.
57 Murata, T. (1989). Petri nets: Properties, Analysis and Applications, Proc. of IEEE, 77(4), 541-580.
58 Nocedal, J. and Wright, S. (2006). Numerical Optimization, 2nd edition. New York: Springer.
59 OASIS WSS TC. (2006). WS-Security Core Specification 1.1, http://www.oasis-open.org/ committees/wss.
60 Object Management Group. (2006). CORBA Basics. OMG. http://www.omg.org/ gettingstarted/corbafaq.htm.
61 O’sullivan, J., Edmond, D. and Hofstede, A.T. (2002). What’s in a service? Towards Accurate Description of Non-Functional Service Properties, Kluwer Academic publishers Distributed and Parallel Databases, 12, 117-133.
62 OWL Services Coalition. (2005). OWL-S: Semantic Markup for Web Service Version 1.0, http://www.daml.org/services/owl-s/1.0/owl-s.html.
63 Papadimitriou, C.H. and Steiglitz, K. (1998). Combinatorial Optimization: Algorithms and Complexity, Dover Publications.
64 Papazoglou, M.P. and Georgakopoulos, D. (2003). Service-Oriented Computing, Communications of the ACM, 46(10), 25-28.
65 Papazoglou, M.P., Traverso, P., Dustdar, S. and Leymann, F. (2007). Service-Oriented Computing: State of the Art and Research Challenges, IEEE Computer, 40(11), 38-45.
66 Pareto, V. (1896). Cours D’Economie Politique, Volume I and II.
67 Pasley, J. (2005). How BPEL and SOA Are Changing Web Services Development, IEEE Internet Computing, 9(3), 60-67.
68 Paul, R. (2005). DoD towards Software Services, in Proc. of IEEE International Workshop on Object-oriented Real-time Dependable Systems, 3-6.
69 Paulson, L.D. (2006). Services Science: A New Field for Today’s Economy, IEEE Computer, 18-21.
70 Pezzini, M. (2003). Composite Applications Head toward the Mainstream. http://www.gartner.com.
71 Reuter, H. (1990). An approximation method for the efficiency set of multi-objective programming problems. Optimization, 2(1), 905–911.
72 Rust, R.T. and Miu, C. (2006). What Academic Research Tells Us About Service, Communications of the ACM, 49(7), 49-54.
73 Sheehan, J. (2006). Understanding Service Sector Innovation, Communications of the ACM, 49(7), 43-47.
74 Staab, S. (ed.) (2003). Web Services: Been There, Done That, IEEE Intelligent Systems, 18(1), 72-85.
75 Sullivan, T. (2002). Auto Industry Gets Web Services-Based Dealer Hub. http://www. infoworld.com/article/02/01/28/020128hndealersphere_1.html.
76 Sundaram, R.K. (1996). A First Course in Optimization Theory. New York: Cambridge University Press.
77 Syntelinc.com (2005). Analysts Predict Modest Growth IT Budgets to Hide Some Major Shifts below Surfaces, http://www.syntelinc.com/syntelligence/index.aspx?id=545.
78 Thompson, H.S., Beech, D., Maloney, M. and Mendelsohn, N. (2004). XML Schema Part 1: Structures Second Edition. http://www.w3.org/TR/xmlschema-1/
79 Tosic, V. and Pagurek, B. (2005). On Comprehensive Contractual Descriptions of Web Services, in Proc. of Enterprise Computing, e-Commerce, e-Service, 444-449.
80 Tosic, V., Pagurek, B., Patel, K., Esfandiari, B. and Ma, W. (2005). Management Applications of the Web Service Offerings Language (WSOL), Springer-Verlag, Proc. of CAiSE03, 468-484.
81 TRUST. (2005). Team for Research in Ubiquitous Secure Technology (TRUST), http://trust.eecs.berkeley.edu/
82 Trusted Computing Group. (2005). Trusted Computing Group, Trusted Computing Group, https://www.trustedcomputinggroup.org/
83 Tsai, W.T. (2005). Service-Oriented System Engineering: A New Paradigm, in Proc. of IEEE International Workshop on Service-Oriented System Engineering, 3-6.
84 Tsai, W.T., Chen, Y.N., Bitter, G. and Miron, D. (2006). Introduction to Service-Oriented Computing, ASU Workshop on Service-Oriented Architecture Education, Research and Applications. http://www.public.asu.edu/~ychen10/activities/SOAWorkshop/.
85 Wang, G., Chen, A., Wang, C., Fung, C. and Uczekaj, S. (2004). Integrated Quality of Service (QoS) Management in Service-Oriented Enterprise Architecture, in Proc. of IEEE Enterprise Computing Conference, 21-32.
86 Wikipedia. (2006). Quality of Service. http://en.wikipedia.org/wiki/Quality_of_service.
87 Wikipedia. (2007). Metric Space. http://en.wikipedia.org/wiki/Metric_space.
88 Workflow Management Coalition (2007). XML Processing Definition Language (XPDL). http://www.wfmc.org/standards/xpdl.htm.
89 Yang, S., Lan, B., Chen, I., Wu, B. and Chang, A. (2005). Context Aware Service Oriented Architecture for Web-Based Learning, Advanced Technology for Learning, 2(4), 216-222.
90 Yang, S., Zhang, J. and Lan, B. (2007). Service Level Agreement-Based QoS Analysis for Web Services Discovery and Composition, International Journal of Internet and Enterprise Management, 5(1), 39-58.
91 Yu, T. and Lin, K.J. (2004). Service Selection Algorithms for Web Services with End-to-End QoS Constraints, in Proc. Of IEEE International Conference on E-Commerce Technology (CEC), 129-136.
92 Yu. T. and Lin, K.J. (2005). Service Selection Algorithms for Web Services with End-to-End QoS Constraints, Springer Journal of Information Systems and E-Business Management, 3(2), 103-126.
93 Zadeh, L.A. (1963). Optimality and Nonscalar-Valued Performance Criteria, IEEE Trans. on Automatic Control, AC-8(1), 59-60.
94 Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J. and Sheng, Q.Z. (2003). Quality Driven Web Services Composition, in Proc. of International World Wide Web Confenrece (WWW), 411-421.
95 Zeng, L., Benatallah, B., Ngu, A.H.H., Dumas, M., Kalagnanam, J. and Chang, H. (2004). QoS-Aware Middleware for Web Services Composition, IEEE Transaction on Software Engineering, 30(5), 311-327.
96 Zhang, J. (2005). Trustworthy Web Services: Actions for Now, IEEE IT Pro, 7(1), 32-36.
97 Zhang, J., Zhang, L.J. and Chung, J.Y. (2004a). An Approach to Help Select Trustworthy Web Services, in Proc. of IEEE CEC-East, 84 – 91.
98 Zhang, J., Zhang, L.J. and Chung, J.Y. (2004b). WS-Trustworthy: A Framework for Web Services Centered Trustworthy Computing, in Proc. of IEEE SCC, 186-193.
99 Zitzler, E., Laumanns, M. and Bleuler, S. (2004). A Tutorial on Evolutionary Multiobjective Optimization. In X. Gandibleux, M. Sevaux, K. Sörensen and V. T'kindt (editors), Metaheuristics for Multiobjective Optimisation, Springer. Lecture Notes in Economics and Mathematical Systems Vol. 535, 3-37.
100 Zysman, J. (2006). The Algorithmic Revolution – The Fourth Service Transformation, Communications of the ACM, 49(7), 48.

指導教授

楊鎮華(Jenn-Hwa Yang)

審核日期

2008-6-27

推文