Supporting data center management through clustering of system data streams

Aggregating large data sets related to hardware and software resources into clusters is at the basis of several operations and strategies for management and control. High variability and noise characterizing data collected from system resources monitoring prevent the application of existing solutions that are affected by low accuracy and scarce robustness. We present a new algorithm which extends the clustering method to data center management because it is able to find groups of related objects even when correlation is hidden by high variability. Our experimental evaluation performed on both synthetic and real data shows the accuracy and robustness of the proposed solution, and its ability in clustering servers with correlated functionality