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.
Recently, web service composition technology becomes familiar and it raise the quality offered by the systems designed followed by the service oriented architecture (SOA) framework. Web service composition mainly functioning in dynamic environment, susceptible to the incidence of unpredictable disruption and modifications which could influence the performance of the system. Therefore, the ability to self-healing and manage the execution of web service composition can enhance the reliability and fault tolerance of the system.This study develops an Optimal Deep Learning based Self Healing Mechanism with Failure Prediction (ODL-SHMFP) model for Web Services. The proposed ODL-SHMFP technique aims to accomplish a self healing model for minimizing the failure in web services.
The Cloud Computing uses high speed broadband for good Quality of Service (QoS) so that Cloud based application can be used with high speed which entails the minimum response time, less latency rate and reduced amount of loss of packets. Because of the ample range within the delivered Cloud solutions, from the customer's aspect, it's emerged as irksome to decide whose providers they need to utilize and then what's the thought of his or her option. Bestowing suitable metrics is vital in assessing practices. QoS metrics are playing an important role in selecting Cloud providers and also revamping resource utilization efficiency. To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential requirement. To obtain high quality Cloud applications, Optimal Service Selection is needed. With the increasing number of Cloud services, QoS is usually selected for describing non-functional characteristics of Cloud services. In this paper, a widespread survey on QoS metrics for service vendors and QoS Ranking in Cloud Computing is presented.
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