Towards a Green Approach for Minimizing Carbon Emissions in Fog-Cloud Architecture

Fog computing is developed to complement cloud computing by extending the cloud services (computing, storage, networking, and management) to the edge of the network in order to reduce service latency. Correspondingly, the incremental use of cloud/fog resources and their applications has increased energy consumption and carbon emissions (CO
₂
) of the data centers, which caused significant environmental challenges. Optimizing the placement of the requested resources and applications (e.g., in the form of virtual machines (VMs)) is one of the main solutions, which has a primary effect in reducing the energy consumption of cloud/fog architectures and consequently their CO
₂
emissions. However, due to the geographic distribution of cloud and fog data centers, there are varying levels of CO
₂
emissions to consider, which makes optimizing the placement of resources and applications in distributed cloud/fog more challenging than in centralized clouds in terms of carbon efficiency. In this paper, we propose a multi-level approach using a mixed-integer linear programming (MILP) model to minimize the CO
₂
emissions of data centers by optimizing the resources usage and the placement of VMs in fog-cloud environments. This approach calculates the CO
₂
emissions of the British Telecom (BT) network based on the carbon intensity data from the National Grid ESO, considering several scenarios of traffic demand during different times of the day and year. The results show that the optimal location to host applications highly relies on the carbon intensity and traffic demands. The results also show there is a trade-off between CO
₂
emission reduced by shortening network journey, and CO
₂
emission increased by hosting more applications into the fog nodes. In addition, the results demonstrate that the proposed green fog-cloud architecture outperforms the central cloud and the distributed clouds in terms of reducing the total CO
₂
emission by up to 91% and 71%, respectively. Finally, we develop a heuristic algorithm to mimic and validate the presented work, and it shows comparable results to the MILP model.