Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.

Ensemble Learning Models for Classification and Selection of Web Services: A Review

This paper presents a review of the ensemble learning models proposed for web services classification,selection,and composition.Web service is an evo-lutionary research area,and ensemble learning has become a hot spot to assess web services’earlier mentioned aspects.The proposed research aims to review the state of art approaches performed on the interesting web services area.The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses(PRISMA)as a research method.The study reveals an increasing trend of using ensemble learning in the chosen papers within the last ten years.Naïve Bayes(NB),Support Vector Machine’(SVM),and other classifiers were identified as widely explored in selected studies.Core analysis of web services classification suggests that web services’performance aspects can be investigated in future works.This paper also identified performance measuring metrics,including accuracy,precision,recall,and f-measure,widely used in the literature.

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data centre.Currently,web service composition gains maximum attention among researchers due to its significance in real-time applications.Quality of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS.But these models have failed to handle the uncertainties of QoS.The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users.On the other hand,trip planning is an essential technique in supporting digital map services.It aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the query.But the available web service composition solutions do not consider the complicated spatio-temporal features.For resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location awareness.The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,etc.At the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition plans.Followed by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite plans.Besides,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user rating.An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

Dynamic Service Discovery, Composition and Reconfiguration in a Model Mapping Business Process to Web Services

In any organization where SOA has been implemented, all of the web services are registered in UDDI and users’ needs are served by using appropriate web services. So in this paper, we will try to discover a service from repository first that can provide the required output to the user. The process becomes difficult when a single service is not able to fulfill a user’s need and we need a combination of services to answer complex needs of users. In our paper, we will suggest a simpler approach for dynamic service composition using a graph based methodology. This will be a design time service composition. This approach uses the functional and non-functional parameters of the services to select the most suitable services for composition as per user’s need. This approach involves “service classification” on the basis of functional parameters, “service discovery” on the basis of user’s need and then “service composition” using the selected services on the basis of non-functional parameters like response time, cost, security and availability. Another challenge in SOA implementation is that, once the composition has performed, some services may become faulty at runtime and may stop the entire process of serving a user’s need. So, we will also describe a way of “dynamic service reconfiguration” in our approach that will enable us to identify and replace a faulty service that is violating the SLA or is not accessible anymore. This service reconfiguration is done without redoing or reconfiguring the entire composition. In the end, to simulate the proposed approach, we will represent a prototype application built on php 5.4 using My SQL database at backend.

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Web services are provided as reusable software components in the services-oriented architecture.More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service(QoS)limitations.The workflow consists of tasks where many services can be considered for each task.Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial(NP)-hard problem.This work focuses on the Web Service Composition(WSC)problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey.The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs.It also addresses the Bat Algorithm(BA)shortcomings,such as the tradeoff among exploration and exploitation searching mechanisms,local optima,and convergence rate.The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism.An elitist mechanism is utilized to address the BA convergence rate.The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism.Several benchmark datasets are selected to evaluate the proposed bat algorithm’s performance.The simulation results are estimated using the average fitness value,the standard deviation of the fitness value,and an average of the execution time and compared with four bat-inspired algorithms.It is observed from the simulation results that introduced enhancement obtains significant results.

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

Minimal role mining method for Web service composition

Web service composition is a low cost and efficient way to leverage the existing resource and implementation.In current Web service composition implementations,the issue of how to define the role for a new composite Web service has been little addressed.Adjusting the access control policy for a new composite Web service always causes substantial administration overhead from the security administrator.Furthermore,the distributed nature of Web service based applications makes traditional role mining methods obsolete.In this paper,we analyze the minimal role mining problem for Web service composition,and prove that this problem is NP-complete.We propose a sub-optimal greedy algorithm based on the analysis of necessary role mapping for interoperation across multiple domains.Simulation shows the effectiveness of our algorithm,and compared to the existing methods,our algorithm has significant performance advantages.We also demonstrate the practical application of our method in a real agent based Web service system.The results show that our method could find the minimal role mapping efficiently.