This paper focuses on the problem of workload placement in an InterCloud with the view of minimizing the carbon footprint of such a computing environment. In order to reduce the ecological impact of the data center Greenhouse Gas (GhG) emissions, this paper addresses the problem as a whole, by proposing a global mathematical formulation, based on the joint optimization of the Virtual Machine (VM) placement and their related traffics, along with a workload consolidation method and a cooling maximization technique that considers the dynamic behavior of the cooling fans. As the Virtual Machine Placement Problem (VMPP) is classified as an NP-hard problem, with the addition of the traffic embedding, the problem becomes more complex and stays NP-hard. Therefore, we propose a hybrid approach, for solving such problem and find good feasible solutions in a polynomial time. The results obtained from comparing with the exact method and other reference approaches help in assessing the efficiency of the proposed algorithm, as the carbon footprint costs are relatively close to the lower bound, with an average gap of about 3 percent, and found within a reasonable amount of time.

In this paper, we address the problem of Virtual Machine (VM) placement in an InterCloud with regard to the reduction of carbon footprint in such computing environment. In order to minimize data centers’ Greenhouse Gas (GhG) emissions, this paper proposes a mathematical formulation, where the placement approach is stated as a Mixed-Integer Nonlinear Programming (MINLP) problem which aims at minimizing the carbon footprint of the InterCloud. The proposed formulation presents an accurate carbon footprint evaluation based on joint optimization techniques, such as smart and performance-aware workload consolidation, along with cooling efficiency maximization, while considering the greenness factor of the data centers and the dynamic behavior of the equipment cooling fans. Simulations with an exact method showed that, compared with other baseline approaches, the proposed mathematical model leads to optimal configurations with minimal GhG emissions in a single cloud, as well as in an InterCloud environment, and can yield savings of up to 65 percent. The proposed model has also been compared with an approach that performs blind VM consolidation, and the obtained results showed that such model can simultaneously lead to VM configuration that yields the minimum carbon footprint while performing smart VM consolidation in order to prevent Service Level Agreement (SLA) violations.