A lot of cloud computing and cloud database techniques are adopted in industry and academia to face the explosion of the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, the phenomenon of energy wasting is related to resource provisioning. Hot-N-Cold model is introduced in this paper, which uses workload predictions and DVFS(Dynamic Voltage and Frequency Scaling) to cope with the resource provisioning problem within energy aware cloud database systems. In this model, the resource provisioning problem is considered as two bounded problems. A nonlinear programming algorithm and a multi-phase algorithm are proposed to solve them. The experimental results show that one of the proposed algorithms has great scalability which can be applied to a cloud database system deployed on 70 nodes. Using Hot-N-Cold model can save up to 21.5% of the energy of the running time.