Machine learning approach to data center monitoring using wireless sensor networks

Data Centers face considerable challenges in seamless integration of telemetry and control functions. These functions are essential to management tasks related to power capping, cooling, reliability, predictability, survivability, and adaptability control. It is therefore essential to create an infrastructure of sensors that monitors the physical properties of the dynamically changing environment. The conventional approaches to support distributed sensor data collection and control using wired solutions are static, expensive, and non-scalable. In this paper sensors and control agents supporting this telemetry are a part of a dense and noisy network that are scattered across the data centers. We present an alternative approach for this unique environment using wireless sensor network to improve data efficiency and real-time delivery. We propose genetic algorithm (GA) approach for a densely populated sensor network to dynamically construct optimal collection trees through improved channel diversity that support context aware sensor data compression and reduced latency data delivery.