Paving the Way Toward Energy-Aware and Automated Datacentre

Energy efficiency and datacentre automation are critical targets of the research and deployment agenda of CINECA and its research partners in the Energy Efficient System Laboratory of the University of Bologna and the Integrated System Laboratory in ETH Zurich. In this manuscript, we present the primary outcomes of the research conducted in this domain and under the umbrella of several European, National and Private funding schemes. These outcomes consist of: (i) the ExaMon scalable, flexible, holistic monitoring framework, which is capable of ingesting 70GB/day of telemetry data of the entire CINECA datacentre and link this data with machine learning and artificial intelligence techniques and tools. (ii) The exploitation of ExaMon to evaluates the viability of machine-learning based job scheduling, power prediction and deep-learning based anomaly detection of compute nodes. (iii) The viability of scalable, out-of-band and high-frequency power monitoring in compute nodes, by leveraging low cost and open source embedded hardware and edge-computing, namely DiG. (iv) Finally, the viability of run time library to exploit communication regions in large-scale application to reduce the energy consumption without impairing the execution time, namely COUNTDOWN.