Scheduling computing loads for improved utilization of solar energy

Abstract The rise in the penetration of the internet across the world has led to a rapid increase in the consumption of energy at the data centers established by leading cloud data service providers. High power consumption by these data centers [DCs] leads to high operational costs and high carbon emissions into the environment. From a sustainability point of view, the ultimate goal is to maximize the productivity and efficiency of these data centers while keeping greenhouse gas emissions to the minimum and maximize data center productivity. This goal can be achieved by better resource utilization and replacing carbon-intensive approaches of energy production with green sources of energy. Due to the limited intermittent availability of renewable sources of energy, the ideal ‘Green’ design for the DCs, should incorporate inter-operability with both renewable and non-renewable sources of energy. In this paper, we propose a ren-aware scheduler to schedule computational workload by prioritizing their execution within the duration of green energy availability on the basis of the predicted hourly green energy and workload data of DCs. Our results demonstrate that our ren-aware scheduler can increase the green energy consumption by 51% compared to the conventional randomized scheduler that distributes load without considering green energy and load. It can also reduce the total energy consumption by 25% by putting the DCs to sleep during their idle time, as it saves 4.5 times more idle energy than the randomized scheduler. Additionally, the results also demonstrate how the role of time zones of the DCs and the duration of green energy availability in them is pivotal in our ren-aware scheduler's performance.