SGEESS: Smart green energy-efficient scheduling strategy with dynamic electricity price for data center

• This paper considers a soft real-time task scheduling on a green data center.
• A soft real-time task model, an energy model and a scheduling model are developed.
• A smart green energy-efficient scheduling strategy is proposed.
• The strategy is based on renewable energy prediction and dynamic electricity price.
• The strategy is thoroughly tested and compared with three designed strategies.

Nowadays, it becomes a major trend to use the green renewable energy in the data center when considering the environment protection and the energy crisis. To improve the energy efficiency and save the system cost, the computational tasks of data center should match to the renewable energy supply. This paper aims to develop a smart green energy-efficient scheduling strategy to increase utilization of renewable energy, reduce system running cost and improve the task satisfaction rate in a data center partially powered by the renewable energy. We first define three mathematical models, i.e., task model, energy model and scheduling model for the proposed problem. Then, a smart green energy-efficient scheduling strategy is proposed for the task scheduling in the data center, based on the renewable energy prediction and the dynamic grid electricity price. In the experiments, three other scheduling strategies, i.e., Green-Scheduling Strategy, Price-Scheduling Strategy and Greedy-Energy-Efficient Strategy, are provided for comparisons, a real-world trace of Google cloud trace is also tested. The experimental results confirm the superiority and effectiveness of the proposed scheduling strategy.