Minimizing energy costs in federated datacenters under uncertain green energy availability

The cost of energy represents, by far, the largest fraction of total operational expenditures that datacenter operators ought to face. For this very reason, several studies have focused on evaluating how such energy costs can be reduced and on quantifying that reduction; using green energy sources (e.g. solar) that can be generated by installing infrastructures nearby datacenters is clearly an interesting option. Assuming that green energy is available, workloads consolidation in those datacenters with the highest amount of self-generated energy allows reducing remarkably the consumption of brown energy. Workload management is of paramount importance to increase green energy consumption in the context of distributed datacenters. In that scenario, a centralized and orchestrated operation leads to large energy cost savings. To this end, we firstly present a model to estimate the amount of green energy produced in each location as a function of the specific time period and the expected weather conditions. Next, the problem of minimizing energy costs by properly placing workloads in federated datacenters under uncertainty in the availability of green energy in each location is faced using stochastic programming techniques. Illustrative numerical results validate the usefulness of the proposed stochastic approach.