Abstract-The rapid growth of cloud services has raised concerns on the environmental sustainability of cloud computing, as data centers (DCs) consume a huge amount of brown energy, i.e., energy derived from polluting sources such as coal, oil, natural gas, etc. One way to decrease carbon emissions of DCs is to replace brown energy with green energy, i.e., energy produced by renewable sources, such as wind farms, solar panels, hydroelectric dams, etc. But, to maximize utilization of green energy, effective decisions need to be taken both at the "DC placement" stage (i.e., when placing DCs) and the "DC addition" stage (i.e., when adding new DCs to accommodate traffic growth). The resulting green DC-placement problem is characterized by a tradeoff between brown energy reduction and cost reduction. On one hand, due to geo-diverse locations of renewable energy sources, and due to the need for low latency and high availability for users, a large number of DCs should be placed. On the other hand, capital and operational expenditure would significantly increase if a large number of DC is deployed. In this paper, we propose two solution methods, based on multiobjective optimization, to address the tradeoff between brown energy consumption and cost in cloud networks in both DC placement and DC addition scenarios. We show via simulations how to choose the optimal number of DCs and their locations over two study cases based on NSFNET and USNET topologies.
Wu et al. REF investigated the DC placement problem, the brown energy consumption problem, and DC cost problem in an inter-DC network with VM migration.
Green Data Center Placement in Optical Cloud Networks
IEEE Transactions on Green Communications and Networking
IEEE Transactions on Green Communications and Networking
Engineering Computer Science