Forecasting Quality of Service (QoS) Parameters of IoT based Web Services with an Artificial Electric Field Algorithm trained Artificial Neural Network

Abstract

Development of an accurate forecasting model for effective prediction of Quality of Service (QoS) parameters of inter-net of things (IoT) based web services is highly desired, such that it improves service management and user experience. Mostly, QoS parameters are volatile in nature which make the IoT based service and recommendation process chal-lenging. Artificial neural network (ANN) based models are found to be worthy in modeling and forecasting nonlinear QoS parameter sequences. However, improper tuning of ANN parameters with conventional training algorithms may lead to a suboptimal model. Nature-inspired optimization methods are found suitable in fine tuning ANN parameters and have shown proficient results on real-world data mining problems. There is lack of such models for QoS parameters prediction that need to be explored. We develop an Artificial Electric Field Algorithm (AEFA) trained ANN (AEFANN) model for effective and accurate prediction of QoS parameters where AEFA is used to search an optimal ANN structure. The optimal ANN structure is achieved by AEFA through an evolutionary process. Two real-world IoT enabled web service datasets are used for evaluating effectiveness of AEFANN in terms of three performance metrics. Experimental procedures and comparative studies are conducted to establish the superiority of the proposed approach over four other similar forecasts. AEFANN obtained relative worth values of 4.13% ~ 69.12 % (5-min granularity) and 43.32% ~ 80.3 % (1-hr granularity) from SERVICE 1 dataset. Similarly, it obtained relative worth values of 7.25% ~ 65.57 % (5-min granularity) and 43.38% ~ 72.43 % (1-hr granularity) from SERVICE 2 dataset when compared to oth-er models. This is a significant improvement over comparative existing similar model.