Abstract

Cloud Data Centers (DCs) consume extensive amounts of energy, making a significant
contribution to environmental concerns. Moreover, with the emergence of 5G and future
B5G networks, which are increasingly inclined towards software orientation and reliant
on cloud computing, there is an urgent requirement for optimizing the energy consumption
of DCs. We address this issue by proposing an energy-aware Virtual Machine (VM)
placement solution for energy minimization.
In the first part of this study, we propose a highly accurate model for predicting the
dynamic power consumption of cloud computing devices. Our proposal takes advantage
of the various sensors that are now embedded in physical machines, or more generally in
cloud server machines, as well as Performance Monitoring Counters (PMCs) to implement
a highly accurate Machine Learning (ML) power prediction model. The core part of this
study then integrates the novel feature space of real-time sensors’ measurements and the
predictive power model to propose a scalable placement algorithm, enabling proactive and
energy-aware Virtual Machine placements. In addition, it utilizes a new set of temperature-related
features that enables proactive hotspot avoidance.
Our ML predictive models, as well as our proposed placement algorithm, were extensively
evaluated on a cluster of real physical machines and demonstrated a significantly
higher performance as compared to the implemented reference models and algorithms, reducing
energy consumption by up to 7%, CPU temperature by 2%, and overloading by 28%.