Failure prediction in the internet of things due to memory exhaustion

We present a technique to predict failures resulting from memory exhaustion in devices built for the modern Internet of Things (IoT). These devices can run general-purpose applications on the network edge for local data processing to reduce latency, bandwidth and infrastructure costs, and to address data safety and privacy concerns. Applications are, however, not optimized for all devices and could result in sudden and unexpected memory exhaustion failures because of limited available memory on those IoT devices. Proactive prediction of such failures, with sufficient lead time, allows for adaptation of the application or its safe termination. Our memory failure prediction technique for applications running on IoT devices uses k-Nearest-Neighbor (kNN) based machine learning models. We have evaluated our technique using two third-party applications and a real-world IoT simulation application on two different IoT platforms and on an Amazon EC2 t2.micro instance for both single and multitenancy use cases. Our results indicate that our technique significantly outperforms simpler threshold-based techniques: in our test applications, with 180 seconds of lead time, failures were accurately predicted with 88% recall at 74% precision for a single application failure and 76% recall at 71% precision for multitenancy failure.