Data-Dependent QoS-Based Service Selection

Data analytic applications and services are becoming increasingly important, especially in this age of Big Data. QoS properties such as latency, reliability, response time of such services can vary based on the attributes (e.g.,size, number of dimensions, data types) of the dataset being processed. The existing QoS-based web service selection methods are not adequate for ranking this type of services because they do not consider these dataset attributes. In this paper, we have proposed a method to predict the QoS values for data analytic services based on the attributes of the dataset by incorporating a meta-learning approach. We could then rank these services according to the predicted QoS values. Our experiment results prove the effectiveness of this approach and the improvement in service ranking when compared with the traditional service selection approach.