Health Monitoring and AutoScaling RabbitMQ Queues within the Smart Home System Smart home solutions rely on their cloud components to provide various administrative functionalities and advanced data processing features, such as alarms, historical data storage, notifications, etc. As the complexity of a cloud system increases, its components need monitoring in order to detect anomalies like system unavailability, unusual load, memory overload, and similar. The monitoring tools collect and store system metrics, which are important not only for anomaly detection, but also for root cause analysis and capacity planing. When an anomaly is detected, an alarm of some sort is typically sent to the system administrator. Usually, human interaction is needed in order to remove the anomaly. In this paper we present a software tool that monitors messages between cloud components of the existing smart home system. This tool aims to provide autonomous reaction of the system to potential problems, and enable autoscaling, without human interference.

Anomaly Detection AsaService Cloud systems are complex, large, and dynamic systems whose behavior must be continuously analyzed to timely detect misbehaviors and failures. Although there are solutions to flexibly monitor cloud systems, costeffectively controlling the anomaly detection logic is still a challenge. In particular, cloud operators may need to quickly change the types of detected anomalies and the scope of anomaly detection, for instance based on observations. This kind of intervention still consists of a largely manual and inefficient adhoc effort. In this paper, we present Anomaly Detection asaService (ADaaS), which uses the same asaservice paradigm often exploited in cloud systems to declarative control the anomaly detection logic. Operators can use ADaaS to specify the set of indicators that must be analyzed and the types of anomalies that must be detected, without having to address any operational aspect. Early results with lightweight detectors show that the presented approach is a promising solution to deliver better control of the anomaly detection logic.