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 ...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.展开更多
Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone...Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.展开更多
文摘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.
基金This work has supported by the Xiamen University Malaysia Research Fund(XMUMRF)(Grant No:XMUMRF/2019-C3/IECE/0007)。
文摘Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.