Time-Based QoS Prediction and Rank Aggregation of Web Services

Time-Based QoS Prediction and Rank Aggregation of Web Services

V. Mareeswari (School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India) and E. Sathiyamoorthy (School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, India)
DOI: 10.4018/IJITWE.2019100105
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Everyday activities are equipped with smart intellectual possessions in the modern Internet domain for which a wide range of web services are deployed in business, health-care systems, and environmental solutions. Entire services are accessed through web applications or hand-held computing devices. The recommender system is more prevalent in commercial applications. This research predicts the preference of consumers and lists the recommended services in order of ranking for consumers to choose services in a short time span. This proposed approach aims to offer the exact prediction of missing QoS (quality of service) value of web services at a specified time slice. The uncertainty of QoS value has been predicted using the cloud model theory. The focus is to give the global ranking using the aggregated ranking of the consumer's ranking list, which has been obtained through the Kemeny optimal aggregation algorithm. In this work, multidimensional QoS data of web services have experimented and given an accurate prediction and ranking in the web environment.
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1. Introduction

The current development in Information Technology is motivated by the Internet of Things (IoT) and the further vision is pushing to achieve the smart feature at any time from any media. The Web of Things (WoT) is derived from and associated with the IoT for integrating real-world objects on the web. The WoT operates at the application layer and simplifies the development of IoT applications. The middleware architecture approach as service-oriented architecture (SOA) is inspired and often followed by IoT applications in certain circumstances. The realization of SOA is accomplished through web services. Web services are used in a variety of domains, such as transportation, health care, smart environment, social networking and other futuristic applications (Atzori, Iera & Morabito, 2010).

The WoT reuses existing web standards, approaches, architectural styles and programming patterns, which allows for connecting real-world objects in the World Wide Web. For an example, for NFC (near field communication) equipped mobile phones, browsing the touristic augmented map calls the corresponding web service and provides information about restaurants, petrol stations, emergency service and other related information predicted based on user preference. In particular, the prediction techniques are widely used in the stock market, weather forecasting, public health sector, and other sectors.

Web services are discovered and accessed from UDDI (Universal Description Discovery and Integration) registry, which is standardized by OASIS (Organization for the Advancement of Structured Information Standards). The UDDI registries provide web services in API (Application Programming Interface) format, which is available to all consumers of a specific organization. For example, in 2013 Microsoft announced the BizTalk Server as the depreciation of UDDI. Moreover, web service portals (Al-Masri & Mahmoud, 2008) such as webservicex.net, programmableweb.com and remotemethods.com that are offered services as in API format. Also, cloud services (Sun, Dong, Hussain, Hussain, & Chang, 2014) are closely related to web services which are associated and not to be separate. The best example is the Amazon Web Service platform, which is one of the sectors producing high revenue in the leading company Amazon.

Hence, web services are increasing and adapting to new technology. QoS (Quality of Service) measurements are used to select the best services based on performance. QoS is measured in terms of throughput, response time, availability, scalability, etc. (Ma et al., 2016) apparently differentiate subjective and objective QoS values. The QoS value is given by the consumer based on his or her perceptions and is considered a subjective value; for example, products ratings on Amazon and movies scores on Movie Lens. The objective QoS means a QoS value of a web service can be measured based on characteristics such as response time, throughput and reliability; for example, WSDream dataset holds invocation records with response time and throughput while accessing web services by consumers. Hence, these objective data can be determined based on bandwidth, network traffic and much more. But it changes over time in this Internet-based world. For instance, QoS values vary for the same service accessed by the same consumer at different intervals of time. Therefore, the time of invocation records is significant for QoS-based research, as demonstrated by (Zheng, Zhang, & Lyu, 2014). At the same instant of time span, fluctuation occurs in the QoS value when the same service is accessed by different consumers in different intervals of time.

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