A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm

Abstract

Cloud computing plays an essential role in enabling practical applications based on the Industrial Internet of Things (IIoT). Hence, the quality of these services directly impacts the usability of IIoT applications. To select or recommend the best web and cloud based services, one method is to mine the vast data that are pertinent to the quality of service (QoS) of such services. To enable dynamic discovery and composition of web services, one can use a set of well-defined QoS criteria to describe and distinguish functionally similar web services. In general, QoS is a nonfunctional performance index of web services, and it might be user-dependent. Hence, to fully assess the QoS of all available web services, a user normally would have to invoke every one of them. This implies that the QoS values for services that the user has not invoked would be missing. If the number of web services available is large, it is virtually inevitable for this to happen because invoking every single service would be prohibitively expensive. This issue is typically resolved by employing some predication algorithms to estimate the missing QoS values. In this paper, a data-driven scheme of predicting the missing QoS values for the IIoT based on a kernel least mean square algorithm (KLMS) is proposed. During the data prediction process, the Pearson correlation coefficient (PCC) is initially introduced to find the relevant QoS values from similar service users and web service items for each known QoS entry. Next, KLMS is used to analyze the hidden relationships between all the known QoS data and corresponding QoS data with the highest similarities. We therefore can apply the derived coefficients for the prediction of missing web service QoS values. An extensive performance study based on a public data set is conducted to verify the prediction accuracy of our proposed scheme. This data set includes 200 distributed service users on 500 web service items with a total of 1,858,260 intermediate data values. The experiment results show that our proposed KLMS-based prediction scheme has better prediction accuracy than traditional approaches.