Web Service Recommendation System using History and Quality of Service

Sullivan, Alvin; Candra, Muhammad Zuhri Catur

doi:10.1109/icodse48700.2019.9092755

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Recent work in web service recommendation systems takes into account different characteristics of web services, such as their history [6], geographic location [7], and isolated QoS [8]. However, to the best of our knowledge, there is a lack of approaches facing how to compute the performance of web service compositions from their constituent web services, particularly when complete information about the QoS of the web services is not available.…”

Section: Introductionmentioning

confidence: 99%

Computing performance requirements for web service compositions

García‐Domínguez

Palomo-Lozano

Medina‐Bulo

et al. 2023

Computer Standards & Interfaces

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Section: Introductionmentioning

confidence: 99%

Computing performance requirements for web service compositions

García‐Domínguez

Palomo-Lozano

Medina‐Bulo

et al. 2023

Computer Standards & Interfaces

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Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Gnanasekaran

Chinnasamy

Parasuraman³

2022

Concurrency and Computation

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Quality of service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, performing a highly accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, a hybrid deep learning technique is proposed to predict future QoS parameters. Initially, the data is collected from the WSDream data repository, however, the collaborating filtering (CF) technique is used to pre-process the collected data. Accordingly, the filtering technique resolves missing or undermined values and cleans the node. Additionally, this pre-processing technique reduces the error and promotes prediction efficiency. Subsequently, data clustering is performed by introducing a modified k-medoids algorithm, which groups the user services into different clusters. Accordingly, a future QoS is predicted based on the proposed hybrid deep learning method like convolutional neural network-long short-term memory. The proposed method is implemented using Python software and it evaluates response time, throughput, root mean square error, and mean absolute error (MAE). The proposed method is compared with the existing collaborative filtering-k medoids, support vector regression, self-exciting threshold auto-regressive, genetic guided clustering algorithm algorithms, and novel deep learning-based hybrid approach. The result of the proposed method presents higher accuracy and low consumption time. Therefore, the performance of the proposed method is 6% higher than the other existing methods. Accordingly, the MAE, mean absolute percentage error, mean absolute scaled error, and root mean squared error of the proposed method produces 7%, 7.5%, 2%, and 1.5% higher performance than the other existing methods.Subsequently, the precision and recall of the proposed method are approximately 3.5% and 3% high than the other state of art methods. The experimental results highlight that the viability of the proposed approach contrasted with the regular methodologies in real-time. Consequently, by introducing this proposed method the QoS prediction performance is improved and it provides higher-quality solutions.

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A Hybrid Model for QoS Prediction based on Improved Conditional Variational Autoencoder

Wang

et al. 2022

2022 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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Web Service Recommendation System using History and Quality of Service

Cited by 3 publications

References 9 publications

Computing performance requirements for web service compositions

Computing performance requirements for web service compositions

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

A Hybrid Model for QoS Prediction based on Improved Conditional Variational Autoencoder

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