AC/DCIM: Acoustic Channels for Data Center Infrastructure Monitoring

Data center infrastructure monitoring (DCIM) uses various features to track a data center's state. In addition to collecting device-level information, the monitoring also includes physical features such as temperature or power intake to detect equipment failures and anomalies. Measuring physical features requires dedicated sensors at specific vantage points for efficient and reliable data collection. We propose a novel approach for DCIM using acoustic channels. Audio-based DCIM offers substantial benefits: sensors are affordable, data collection is non-intrusive, and audio processing is well-understood. Information extraction from acoustic channels can be challenging due to audio consisting of multiple devices' mixed and often noisy signals. Our paper demonstrates the feasibility of single-node state detection over audio side-channels. Experiments in a real data center show that our sound-based approach can successfully detect errors.