# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 1 Introduction  
The number and scale of data centers (DCs) are rapidly increasing, as data centers are the primary means through which companies can satisfy their increasing demand for computing and storage resources, either by building private data centers or by offloading applications and services to external cloud providers. A major issue is that data centers consume a large amount of energy and that consumption is expected to increase at a significant rate. It is estimated that data center electricity consumption will increase to roughly 140 billion kilowatt-hours annually by 2020, corresponding to about 50 large power plants, with annual carbon emissions of nearly 150 million metric tons. The financial impact for DC management is also huge, since a DC spends between 30% and 50% of its operational expenditure in electricity: the expected figure for the sector in 2020 is $13 billion per year of electricity bills (updated information can be found on the web portal of the U.S. National Resources Defense Council, [[http://www.nrdc.org/energy/data-center-efficiency-assessment.asp](http://www.nrdc.org/energy/data-center-efficiency-assessment.asp)]([http://www.nrdc.org/energy/data-center-efficiency-assessment.asp](http://www.nrdc.org/energy/data-center-efficiency-assessment.asp))). The efficient utilization of resources in the data centers is therefore essential to reduce costs, energy consumption, carbon emissions and also to ensure that the quality of service experienced by users is adequate and adherent to the stipulated service level agreements. Efficiency is essential not only in single data centers, but also in geographically-distributed data centers, whose adoption is rapidly increasing. Major cloud service providers, such as Amazon, Google and Microsoft, are deploying distributed data centers to match the increasing demand for resilient and low-latency cloud services, or to interconnect heterogeneous data centers owned by different companies, in the so-called \"inter-cloud\" scenario. In this scenario, the dynamic allocation and migration of workload among data centers can help to reduce costs, moving the workload where the energy is less expensive/cleaner and/or cooling costs are lower: the cloud provider has the option of choosing the destination site based on different criteria upon the reception of the user request. Specifically, the increasing adoption of renewable energy plants is a great opportunity for a more efficient management of distributed data centers. Each data center can get its electricity from different electricity providers or can adopt on-site renewable energy sources, which provide green energy such as solar and wind [1]. Moving applications and services to data centers that are equipped with renewable energy sources (RES) can lead to several benefits both for the data center provider, which can reduce the costs of acquiring grid energy, and for the society in general, thanks to a more intense exploitation of green energy and the reduction of carbon emissions. Unfortunately, workload assignment and migration in a distributed environment involve very complex decision processes due the time-variability of electricity cost, the workload variability both within single sites and across the whole infrastructure and, when the adoption of RES is possible, the intermittent nature of green energy generation. In a geographically-distributed scenario, the peak hours of renewable energy generation can be different in each data center, due to the variability of meteorological conditions and the different time zones. This generates the need for moving the workload to the sites where and when the green energy is currently available. Indeed, while energy storage units can be appropriately used to defer the utilization of green energy for some time, this postponement comes at a significant cost related to the charging and discharging of batteries.