GreenFlowing: A Green Way of Reducing Electricity Cost for Cloud Data Center Using Heterogeneous ESDs In this paper, we propose a scheduling scheme called GreenFlowing to reduce the electricity cost for a cloud data center by leveraging heterogeneous ESDs. In our model, the data center can be powered by intermittent green energy like wind and solar, and the electricity with timevarying prices from the power grid. The energy from different sources can also choose to flow into longterm or shortterm ESDs for later use. Note that, the former can sustain energy for a long time but with low chargingdischarging rate, while the latter can chargedischarge very fast but with high energy leakage that can sustain energy only for a few hours. By combining them together, the energy cost can further be reduced. However, it is hard to decide when and how much energy from different sources should be used to power the data center directly or charged into different types of ESDs. We formulate our scheduling into a largescale linear programming (LP) problem, which can be solved using CPLEX. Numerical experiments show that our scheduling can significantly reduce the total electricity cost for a cloud data center.

Opportunistic Scheduling in Clouds Partially Powered by Green Energy The fast growth of demand for computing and storage resources in data centers has considerably increased their energy consumption.  Improving the utilization of data center resources and integrating renewable energy, such as solar and wind, has been proposed to reduce both the brown energy consumption and carbon footprint of the data centers. In this paper, we propose a novel framework oPportunistic schedulIng broKer infrAstructure (PIKA) to save energy in small monosite data centers. In order to reduce the brown energy consumption, PIKA integrates resource overcommit techniques that help to minimize the number of poweredon Physical Machines (PMs). On the other hand,   PIKA dynamically schedules the jobs and adjusts the number of poweredon PMs to match the variable renewable energy supply. Our simulations with a realworld job workload and solar power traces demonstrate that PIKA saves brown energy consumption by up to 44.9% compared to a typical scheduling algorithm.