Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments

Yaghoubi, Mohamadali; Maroosi, Ali

doi:10.1016/j.simpat.2020.102090

Cited by 31 publications

(33 citation statements)

References 31 publications

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“…The authors in (Yaghoubi et al 2020) address the issue of web services composition in cloud computing. It takes into account QoS aspects when composing different services.…”

Section: Analysis Of Academic Research Studiesmentioning

confidence: 99%

Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing

Eisa

Younas

Basu

et al. 2020

Simulation Modelling Practice and Theory

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Cloud service selection process is significantly challenging and complicated as there are various QoS factors to consider when selecting cloud services. This paper proposes a new QoS-aware selection model systematically and succinctly representing QoS attributes which cloud consumers can easily use and understand when selecting cloud services. In order to ensure the credibility of the cloud service selection, the proposed model collects QoS data from different sources including cloud providers, users' reviews and cloud monitoring tools. It implements Multi-Criteria Decision Making models in order to rank services based on various QoS attributes. The proposed model is implemented as a simulation tool which is deployed on Amazon cloud platform. Using the simulation tool, the proposed model is rigorously evaluated through a number of experiments by taking into account data from widely used commercial cloud service providers. The experimental results show that the proposed model ranks and selects cloud services according to the QoS requirements of cloud service consumers. Unlike existing approaches the proposed model takes into account multi-level QoS attributes and data from real cloud providers when ranking cloud services.

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“…The authors in (Yaghoubi et al 2020) address the issue of web services composition in cloud computing. It takes into account QoS aspects when composing different services.…”

Section: Analysis Of Academic Research Studiesmentioning

confidence: 99%

Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing

Eisa

Younas

Basu

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…With the developments of Cloud Computing (CC) and Software as a Service (SaaS), an increased applications and processing resources have been summarized as Web Services (WSs) and provided on the web [1]. The WS consists of cross platform software process, loosely coupled, and flexible, which could tackle various needs for establishing versatile, agile, and cross enterprise applications [2].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Rajeswari¹,

Jayashree²

2022

Computer Systems Science and Engineering

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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.

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“…A considerable body of web services selection has examined the link between QoS metrics and consumers' choice to select web services [6][7][8]. However, service selection based on the QoS metrics has conflict regarding web services selection criteria and performance evaluation methods.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Trust-based Selection of Web Services

2022

KSII TIIS

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Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

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Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments

Cited by 31 publications

References 31 publications

Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing

Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Machine Learning Methods for Trust-based Selection of Web Services

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