Qos-Based Web Service Discovery And Selection Using Machine Learning

Rangarajan, Sarathkumar

doi:10.4108/eai.29-5-2018.154809

Cited by 8 publications

(4 citation statements)

References 32 publications

(36 reference statements)

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“…It reconfigured a composite service given a QoS goal, with a major focus on latency, throughput, accuracy, and data quality. An architecture model for web service discovery and selection was introduced in [31]. The main component in the proposed work is a machine learning-based methodology for predicting the QoS properties using source code metrics.…”

Section: Qos Prediction and Service Selectionmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

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Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

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Section: Qos Prediction and Service Selectionmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

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“…The WS formalization was highlighted, in this work, in terms of enabling the accurate description of its interfaces and parameters, circumstantially streamlining the WS selection process at the syntactic level. In [11], a method to discover and select services using machine learning has been proposed. The system predicts new QoS values from information found in its web service description language (WSDL).…”

Section: Web Service Compositionmentioning

confidence: 99%

QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS

Adarme

Jimeno

2021

Applied Sciences

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Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain’s pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services.

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“…For this, a service-oriented approach i.e., Service Oriented Architecture (SOA) is considered. This scheme is being used in recent research works such as [3,4,8,36].…”

Section: Context Aware Qos Trust Managementmentioning

confidence: 99%

CATSWoTS: Context Aware Trustworthy Social Web of Things System

Javaid

Afzal

Arif

et al. 2019

Sensors

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The inevitable revolution of the Internet of Things (IoT) and its benefits can be witnessed everywhere. Two major issues related to IoT are the interoperability and the identification of trustworthy things. The proposed Context-Aware Trustworthy Social Web of Things System (CATSWoTS) addresses the interoperability issue by incorporating web technologies including Service Oriented Architecture where each thing plays the role of a service provider as well as a role of service consumer. The aspect of social web helps in getting recommendations from social relations. It was identified that the context dependency of trust along with Quality of Service (QoS) criteria, for identifying and recommending trustworthy Web of Things (WoT), require more attention. For this purpose, the parameters of context awareness and the constraints of QoS are considered. The research focuses on the idea of a user-centric system where the profiles of each thing (level of trustworthiness) are being maintained at a centralized level and at a distributed level as well. The CATSWoTS evaluates service providers based on the mentioned parameters and the constraints and then identifies a suitable service provider. For this, a rule-based collaborative filtering approach is used. The efficacy of CATSWoTS is evaluated with a specifically designed environment using a real QoS data set. The results showed that the proposed novel technique fills the gap present in the state of the art. It performed well by dynamically identifying and recommending trustworthy services as per the requirements of a service seeker.

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Qos-Based Web Service Discovery And Selection Using Machine Learning

Cited by 8 publications

References 32 publications

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS

CATSWoTS: Context Aware Trustworthy Social Web of Things System

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