随着全球数据中心和IT系统的电能消耗的不断上升，能耗优化已经成为当前云数据中心亟待解决的重要问题。目前已提出的能耗优化调度方法大部分是针对同构服务器，不能很好地降低异构云环境下（如云数据中心）服务器能耗。为此，本项目给异构物理服务器引入异构能耗约束，研究建立面向云数据中心的异构服务器能耗优化资源分配模型，并提出资源等价优化的求解方法，在资源分配搜索时对等价资源进行剪枝处理，减少资源分配模型的求解空间和加速模型的求解；为了降低大规模云资源调度的复杂度，研究基于划分服务器集群的可扩展分布式调度方法，实现面向云数据中心的可扩展高能效的资源分配模式；在前期云任务调度研究的基础上，研究集成能耗优化和性能优化的云任务调度模型与算法，实现面向不同需求的任务调度，提高异构云环境下任务调度的能效比。本项目的研究不仅对异构云环境的调度理论发展有重要意义，而且对打造绿色节能的云数据中心有重要的应用意义。

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. Most of the existing energy-efficient scheduling approaches are focused on homogeneous servers, and therefore they did not do well in reducing power consumption of servers in heterogeneous cloud environments (such as cloud data centers). By introducing heterogeneous energy constraints of heterogeneous physical servers, a energy-efficient resource scheduling model for heterogeneous physical servers in cloud data centers is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. To reduce the complexity of large-scale cloud resource scheduling, will try to propose a scalable distributed scheduling method based on partition-based server clusters and implement a scalable energy-efficient resource allocation scheme for cloud data centers. On the basis of previous research works in cloud computing task scheduling, the task scheduling model and corresponding algorithm combining energy optimization and performance optimization are proposed to meet the needs for different task scheduling and improve the energy efficiency ratio of task scheduling in heterogeneous cloud environments. The study of the project is important not only for the development of cloud computing resource scheduling theory in heterogeneous cloud environments, but also to build green cloud data centers.