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

Abstract In a heterogeneous Cloud network scenario where a Cloud computing data center serves mobile Cloud computing requests, Cloud providers are expected to implement more innovative and effective solutions for a list of long standing challenges. Energy efficiency in the Cloud data center is one of the more pressing issues near the top of that list. Cloud providers are in constant pursuit of a system that satisfies client demands for resources, maximizes availability and other service level agreement metrics while minimizing energy consumption and, in turn, minimizing Cloud providers' cost. 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.