Fast Synthesis of Power and Temperature Profiles for the Development of Data-Driven Resource Managers
Ivan Ukhov, Diana Marculescu, Petru Eles, Zebo Peng · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2017
The goal of this work is to facilitate the development of proactive power- and temperature-aware resource managers that leverage machine learning in order to attain their objectives. In this context, the availability of sufficiently large amounts of relevant data, which are essential for learning and, therefore, exploration of research ideas, is elusive. In order to fulfill the need, we present a toolchain for fast generation of realistic power and temperature profiles of computer systems. The toolchain provides profuse representative data to learn from during development stages. The overreaching objective is to help research by making it tractable to experiment with the highly promising but data-demanding state-of-the-art techniques for prediction.