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FEWSS-FRAMEWORK TO EVALUATE THE SERVICE SUITABILITY AND pRIVACY IN A DISTRIBUTED WEB SERVICE ENVIRONMENT

International Journal of Modeling, Simulation, and Scientific Computing 2014年第1期5卷 68-104页

作者： D.CHANDRAMOHAN T.VENGATTARAMAN p.dhavachelvan R.BASKARAN V.S.K.VENKATACHALApATHY Department of Computer Science pondicherry University PondicherryIndia Department of Computer Science and Engineering Anna University ChennaiIndia Department of Mechanical Engineering Sri Manakula Vinayagar Engineering College pondicherry India

This paper focuses on addressing the problem of web service monitoring by evaluating its suitability and privacy in a distributed web service environment(DWS).The need for web services monitoring and evaluation is necessary because the quantity and quality of retrieved web services generally does not fulfill the demands or requirements of the web service *** the literature,many authors proposed suitable solutions for checking the quality of web service in an ad hoc scenario but there is no available testbed for this *** this work,it is proposed to develop a framework as a testbed for evaluating the web service for its suitability and *** order to verify,the retrieved web services fulfill the demands and requirements of the requester in a DWS *** Framework to Evaluate the Web Service Suitability(FEWSS)supports service providers in modeling the services with testbed and to design service behavior to comply the service *** generates run-time instances of web services,client’s requests,service registries and other entities in order to emulate realistic SOA *** generating a real testbed,our approach assists in runtime test for provider system/*** focus has been put on the privacy policy extensibility to allow the service providers and users in a complex *** also provides an intuitive interface for testing all services under SOA control.

关键词： Distributed web service privacy service-oriented architecture quality of service service evaluation web service selection UDDI

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GpS： a constraint-based gene position procurement in chromosome for solving large-scale multiobjective multiple knapsack problems

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Frontiers of Computer Science 2018年第1期12卷 101-121页

作者： Jayanthi MANICASSAMY Dinesh KARUNANIDHI Sujatha pOTHULA Vengattaraman THIRUMAL dhavachelvan pONNURANGAM Subramanian RAMALINGAM Department of Computer Science Pondicherry University Pondicherry 605 014 India

The multiple knapsack problem （MKp） forms a base for resolving many real-life problems. This has also been considered with multiple objectives in genetic algorithms （GAs） for proving its efficiency. GAs use self- adaptability to effectively solve complex problems with constraints, but in certain cases, self-adaptability fails by converging toward an infeasible region. This pitfall can be resolved by using different existing repairing techniques; however, this cannot assure convergence toward attaining the optimal solution. To overcome this issue, gene position-based suppression （GpS） has been modeled and embedded as a new phase in a classical GA. This phase works on the genes of a newly generated individual after the recombination phase to retain the solution vector within its feasible region and to im- prove the solution vector to attain the optimal solution. Genes holding the highest expressibility are reserved into a subset, as the best genes identified from the current individuals by re- placing the weaker genes from the subset. This subset is used by the next generated individual to improve the solution vec- tor and to retain the best genes of the individuals. Each gene＇s positional point and its genotype exposure for each region in an environment are used to fit the best unique genes. Further, suppression of expression in conflicting gene＇s relies on the requirement toward the level of exposure in the environment or in eliminating the duplicate genes from the *** MKp benchmark instances from the OR-library are taken for the experiment to test the new model. The outcome por- trays that GpS in a classical GA is superior in most of the cases compared to the other existing repairing techniques.

关键词： combinatorial problems evolutionary algo-rithm multiobjective problems multiple knapsack problem,gene position effect gene suppression

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