An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures

In this work, we introduce a novel mathematical optimization model to solve the problem of energy efficiency in a cloud data center. Next, we offer a solution based on VM migration that tackles this problem and minimizes energy efficiency in comparison to other common solutions. This solution includes a novel proposed technique to be integrated in any consolidation-based energy efficiency solution. This technique depends on dynamic idleness prediction (DIP) using machine learning classifiers. Moreover, we offer a robust energy efficiency scheduling solution that does not depend on live migration. This technique, termed Smart VM Over Provision (SVOP), offers a major advantage to cloud providers in the cases when live migration of VMs is not preferred due to its effects on performance. We evaluate the aforementioned solutions in terms of a number of critical metrics, namely, energy used per server, energy used per served request, acceptance rate, and the number of migrations performed.