Predicting Transient Overloads in Real-Time Systems using Artificial Neural Networks

The emerging need for dynamically scheduled real-time systems requires methods for handling transient overloads. Current methods have in common that they deal with transient overloads as they occur, which gives the real-time system limited time to react to the overload. In this work we enable new approaches to overload management. Our work shows that artificial neural networks (ANNs) can predict future transient overloads. This way the real-time system can prepare for a transient overload before it actually occurs. Even though the artificial neural network is not yet integrated into any system, the results show that ANNs are able to satisfactory distinguish different workload scenarios into those that cause future overloads from those that do not. Two ANN architectures have been evaluated, one standard feed-forward ANN and one recurrent ANN. These ANNs were trained and tested on sporadic workloads with different average arrival rates. At best the ANNs are able to predict up to 85% of the transient overloads in the test workload, while causing around 10% false alarms