An interval type-2 fuzzy model of compliance monitoring for quality of web service

Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.

[1]  Sherif Sakr,et al.  A Framework for Consumer-Centric SLA Management of Cloud-Hosted Databases , 2015, IEEE Transactions on Services Computing.

[2]  Patricia Melin,et al.  Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm , 2018, Algorithms.

[3]  Karim Djemame,et al.  Enabling service-level agreement renegotiation through extending WS-Agreement specification , 2014, Service Oriented Computing and Applications.

[4]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[5]  Lina Wang,et al.  Feature Weighting Fuzzy Clustering Integrating Rough Sets and Shadowed Sets , 2012, Int. J. Pattern Recognit. Artif. Intell..

[6]  Salima Benbernou,et al.  A Soft Constraint-Based Approach to QoS-Aware Service Selection , 2010, ICSOC.

[7]  Fangchun Yang,et al.  A Dynamic Web Service Composition Algorithm Based on TOPSIS , 2011, J. Networks.

[8]  Ricardo Massa Ferreira Lima,et al.  A quality-driven approach for resources planning in Service-Oriented Architectures , 2015, Expert Syst. Appl..

[9]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Jerry M. Mendel,et al.  A new method for managing the uncertainties in evaluating multi-person multi-criteria location choices, using a perceptual computer , 2012, Ann. Oper. Res..

[11]  Quan Z. Sheng,et al.  Web Service Compositions with Fuzzy Preferences: A Graded Dominance Relationship-Based Approach , 2014, TOIT.

[12]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[13]  Enrico Prenesti,et al.  Trueness, precision and accuracy: a critical overview of the concepts as well as proposals for revision , 2015, Accreditation and Quality Assurance.

[14]  Dongrui Wu,et al.  Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers , 2006, Eng. Appl. Artif. Intell..

[15]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[16]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.

[17]  S. Chandramathi,et al.  Qos based Selection and Composition of Web Services-a fuzzy Approach , 2014, J. Comput. Sci..

[18]  Giuseppe Di Modica,et al.  Dynamic SLAs management in service oriented environments , 2009, J. Syst. Softw..

[19]  Rodney Carlos Bassanezi,et al.  A Study on Subjectivities of Type 1 and 2 in Parameters of Differential Equations , 2015 .

[20]  Jerry M. Mendel,et al.  Uncertainty measures for interval type-2 fuzzy sets , 2007, Inf. Sci..

[21]  Peter-Th. Wilrich,et al.  Robust estimates of the theoretical standard deviation to be used in interlaboratory precision experiments , 2007 .

[22]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[23]  Min Chen,et al.  Anytime QoS-aware service composition over the GraphPlan , 2013, Service Oriented Computing and Applications.

[24]  Rajeev Agrawal,et al.  Integrated network selection scheme for remote healthcare systems , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[25]  James C. Bezdek,et al.  Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence] , 1997, IEEE Trans. Fuzzy Syst..

[26]  Ryszard Kowalczyk,et al.  AutoSLAM – A policy‐based framework for automated SLA establishment in cloud environments , 2015, Concurr. Comput. Pract. Exp..

[27]  Xavier Franch,et al.  Monitoring the service-based system lifecycle with SALMon , 2015, Expert Syst. Appl..

[28]  Albert Benveniste,et al.  Probabilistic QoS and Soft Contracts for Transaction-Based Web Services Orchestrations , 2008, IEEE Trans. Serv. Comput..

[29]  Kewen Li,et al.  The application of fuzzy regression based on the trapezoidal fuzzy numbers to the software quality evaluation , 2012 .

[30]  A. Wahab,et al.  IMPLEMENTATION OF SERVICE ORIENTED ARCHITECTURE USING ITIL BEST PRACTICES , 2015 .

[31]  Bo Zhang,et al.  Automatic Fuzzy Rules Generation Using Fuzzy Genetic Algorithm , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[32]  Weina Wang,et al.  On fuzzy cluster validity indices , 2007, Fuzzy Sets Syst..

[33]  Sanjay Kumar Dubey,et al.  Comparative Analysis of K-Means and Fuzzy C- Means Algorithms , 2013 .

[34]  Ping Li,et al.  Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network. , 2019, ISA transactions.

[35]  Preecha Pangsub,et al.  An adaptive type-2 fuzzy for control policing mechanism over high speed networks , 2010, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[36]  Michalis Vazirgiannis,et al.  On Clustering Validation Techniques , 2001, Journal of Intelligent Information Systems.

[37]  David Allenotor,et al.  A Fuzzy Grid-QoS Framework for Obtaining Higher Grid Resources Availability , 2008, GPC.

[38]  Jinjun Chen,et al.  Towards a trust evaluation middleware for cloud service selection , 2017, Future Gener. Comput. Syst..

[39]  Lorenzo Livi,et al.  Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction , 2015, Soft Comput..

[40]  Dongrui Wu.  An overview of alternative type-reduction approaches for reducing the computational cost of interval type-2 fuzzy logic controllers , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[41]  Miin-Shen Yang,et al.  A cluster validity index for fuzzy clustering , 2005, Pattern Recognit. Lett..

[42]  Javier Tuya,et al.  Coverage-Based Testing for Service Level Agreements , 2015, IEEE Transactions on Services Computing.

[43]  Boudewijn P. F. Lelieveldt,et al.  A new cluster validity index for the fuzzy c-mean , 1998, Pattern Recognit. Lett..

[44]  T. Miranda Lakshmi,et al.  An information delivery model for banking business , 2014, Int. J. Inf. Manag..

[45]  Dessislava Petrova-Antonova,et al.  QoS-Aware Web Service Selection Accounting for Uncertain Constraints , 2014, 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications.

[46]  Zeng-qi Sun,et al.  Improved validation index for fuzzy clustering , 2005, Proceedings of the 2005, American Control Conference, 2005..

[47]  Ying-Hong Wang,et al.  Why or Why Not Service Oriented Architecture , 2009, 2009 IITA International Conference on Services Science, Management and Engineering.

[48]  Chandan Giri,et al.  Fuzzy Logic Based Implementation for Forest Fire Detection Using Wireless Sensor Network , 2014 .

[49]  Stuart E. Madnick,et al.  A Context-Based Approach to Reconciling Data Interpretation Conflicts in Web Services Composition , 2013, TOIT.

[50]  Ping Chen,et al.  A fuzzy genetic algorithm for QoS multicast routing , 2003, Comput. Commun..

[51]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[52]  Keqing He,et al.  A Clustering Method for Web Service Discovery , 2011, 2011 IEEE International Conference on Services Computing.

[53]  Chee Peng Lim,et al.  Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[54]  Shonali Krishnaswamy,et al.  A fuzzy model for reasoning about reputation in web services , 2006, SAC.

[55]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[56]  Zahir Irani,et al.  Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector , 2016, Annals of Operations Research.

[57]  Xinwang Liu,et al.  A Survey of Continuous Karnik-Mendel Algorithms and Their Generalizations , 2013, Advances in Type-2 Fuzzy Sets and Systems.

[58]  R. P. Sundarraj,et al.  A model for standardizing human decisions concerning service-contracts management , 2006, Ann. Oper. Res..

[59]  Shangguang Wang,et al.  Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing , 2014, J. Intell. Manuf..

[60]  Adil Baykasoğlu,et al.  A fuzzy multiple-attribute decision making model to evaluate new product pricing strategies , 2015, Annals of Operations Research.

[61]  Lyes Khoukhi,et al.  Multimedia Support in Wireless Mesh Networks Using Interval Type-2 Fuzzy Logic System , 2014, 2014 6th International Conference on New Technologies, Mobility and Security (NTMS).

[62]  Chen Ding,et al.  User-centered design of a QoS-based web service selection system , 2011, Service Oriented Computing and Applications.

[63]  Michal Jakubczyk,et al.  Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment , 2017, Ann. Oper. Res..

[64]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[65]  Barbara Gladysz,et al.  Fuzzy-probabilistic PERT , 2017, Ann. Oper. Res..

[66]  Türkay Dereli,et al.  Industrial applications of type-2 fuzzy sets and systems: A concise review , 2011, Comput. Ind..

[67]  Abdulkadir Sengur,et al.  Comparison of clustering algorithms for analog modulation classification , 2006 .

[68]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[69]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..

[70]  P. Melin,et al.  5 Design of Intelligent Systems with Interval Type-2 Fuzzy Logic , 2007 .

[71]  Karim Djouani,et al.  A Type-2 Fuzzy Logic Decision System for Call Admission Control in Next Generation Mobile Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[72]  Kuldip Singh Sangwan,et al.  Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors , 2016, Annals of Operations Research.

[73]  Pranab K. Muhuri,et al.  Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets , 2019, Eng. Appl. Artif. Intell..

[74]  Guandong Xu,et al.  Social network-based service recommendation with trust enhancement , 2014, Expert Syst. Appl..

[75]  Nagesha,et al.  QoS mapping from user to network requirements in WMSN: A fuzzy logic based approach , 2014, 2014 IEEE International Advance Computing Conference (IACC).