Forecasting QoS Attributes Using LSTM Networks

Many modern software systems and applications are built using heterogeneous services provided by a range of devices, from high-power devices located in the Cloud to potentially resource-constrained and/or mobile services from IoT devices at the edge of the network. The large growth in the number of these services has led to some functionally similar services. When selecting services, a critical criterion is Quality of Service (QoS), which includes factors such as response time, location and cost. As the value of dynamic QoS attributes vary with time, there is a need to accurately forecast future QoS values to identify if a service may be about to fail. In this paper, we propose using an LSTM-based neural network to forecast future QoS values. We evaluate the use of an LSTM network against the existing state of the art in experiments using an established web service dataset and a new dataset collected by deploying services on low power IoT devices, which we publicly release. This mixture of datasets covers the heterogeneity that would be expected in a typical IoT environment.