Comparative study of skyline algorithms for selecting Web Services based on QoS

Fariss, Mourad; Asaidi, Hakima; Bellouki, Mohamed

doi:10.1016/j.procs.2018.01.138

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…For comparing both approaches, MF is the traditional de-facto model for RS, DNN is the new go-to model for RS, however, it usually requires a lot of training data. In some recent studies [102][103][104], people have found out that MF can achieve similar or better performance than DNN.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Service Recommendation Based on Accuracy Related QoS Attributes

Alghofaily

2023

Preprint

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<p>Machine Learning (ML) is a topic of interest in computer science. Amazon AWS, Google Cloud and Microsoft Azure are examples of cloud platforms that provide ML services. Analysts can easily analyze data using online ML services, and most of the model processing is done using cloud services to provide fast and reliable results. Many of these services offer similar functionalities, and the challenge is how to select the best service for the data. Traditional approaches for service-selection depend only on general understanding of the service functionality. A better way is to consider both functional and non-functional requirements. Functional requirements determine overall behaviour of a service. Non-functional requirements establish how relevant a service is to the user’s query and refer to the Quality of Service (QoS) attribute. QoS- based service-selection has been studied in the service computing community for some time. However, characteristics of the input dataset are not usually considered in the selection process even though they might affect the QoS values of the service. In this dissertation, we investigate the impact of adding dataset features and other side information on the performance of QoS prediction and service recommendation. We focus on ML services since their QoS values are potentially highly dependent on the dataset.</p> <p>We propose two approaches for ML service recommendation and compare their performances. The first approach uses factorization for web service recommendation. We identify latent features of the datasets and the services and then recommend services by exploiting these latent variables. The second approach uses neural networks to identify latent features. We also integrate two sets of side information (dataset and service) in both approaches, and study the effect of these added features. In the experiment, we test our system using the real QoS data of 24 classification models running on 390 datasets downloaded from OpenML. In both implementations, models with side information outperform the basic model. To guarantee the best performance for our model, adding side information is necessary, which increases the predictive accuracy by 5% to 25%. Thus, we recommend integrating the side information in recommender systems, specifically including dataset features when recommending ML services. </p>

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Section: Discussionmentioning

confidence: 99%

Machine Learning Service Recommendation Based on Accuracy Related QoS Attributes

Alghofaily

2023

Preprint

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“…For example, SA= {S1, S2, S5} is a set of services that provides task A with QoS= {3, 4, 2}in time and SB= {S2, S4} that provides task B with QoS= {4, 5} in time. The Skyline service for SA= S5 and for SB=S2 are not dominated by other services, and it is the best candidate service [28,[32][33].…”

Section: The Skyline Service Algorithmmentioning

confidence: 99%

Services Composition in Multi-cloud Environments using the Skyline Service Algorithm

Heidari

Emadi

2021

IJE

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“…The authors of [11] compared two algorithms: the BBS and the SFS algorithms on the service web selection. The service selection system was performed efficaciously and reliably by the BBS algorithm as the experimental outcomes show.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Authors in [12] were the first to propose the filtering of web services system named "F-WebS system". The system builds the performance on the description and discovery area of web services [11,[13][14][15] as semantics-based web service filtering and utilizes a variety of matching algorithms such as those in the discovery task.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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An Improved Approach for QoS Based Web Services Selection Using Clustering

Fariss¹,

Allali²,

Asaidi³

et al. 2021

Adv. sci. technol. eng. syst. j.

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With the rising number of web services created to build complex business processes, selecting the appropriate web service from a large number of web services respond to the same client request with the same functionality are developed independently but with different quality of service (QoS) attributes. From this point, there are many approaches to web service selection. Nevertheless, this is still deficient due to a considerable number of discovered web services. The prefiltering is a solution to reduce the number of web services candidates. In this paper, the K-means clustering is applied to determine similar services based on QoS information. The results of this prefiltering are considered at the selection task using the Branch and Bound Skyline (BBS) algorithm. The experimental evaluation performed on real Dataset proves that our approach presents efficient results for web service selection.

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Comparative study of skyline algorithms for selecting Web Services based on QoS

Cited by 13 publications

References 16 publications

Machine Learning Service Recommendation Based on Accuracy Related QoS Attributes

Machine Learning Service Recommendation Based on Accuracy Related QoS Attributes

Services Composition in Multi-cloud Environments using the Skyline Service Algorithm

An Improved Approach for QoS Based Web Services Selection Using Clustering

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