贪心算法优化云数据中心的虚拟机分配策略

如何将云客户端的大量虚拟机均匀的分配到云数据中心的物理主机上执行是一个关键问题.提出了贪心算法优化云数据中心的虚拟机分配策略,首先设计一个用于企业的云数据中心的工作场景,该场景包括三层云计算系统结构,包括用户层、云服务提供者层和云数据中心集合层.用户层用来生成虚拟机的请求集;云服务提供者层通过经典的装箱问题算法完成用户层的大量的虚拟机请求集到底层的云数据中心的分配.然后建立虚拟机分配过程中各种约束因素的数学模型;最后利用贪心算法优化云数据中心之间的虚拟机分配.利用某个企业的大数据中心作为云端测试环境,测试结果表明,经典的最好适应算法Best-Fit-Algorithm具有比较的虚拟机分配效果,云平台的能量消耗比较少,该实验结果对于其他企业构造云数据中心有比较好参考价值.

Greedy Algorithms Optimized Virtual Machine Allocation for Cloud Data Centers

It is a critical problem to uniformly allocate a large number of virtual machines at the cloud clients to the physical hosts at the cloud data centers. To this end, a greedy algorithm optimized virtual machine allocation approach for cloud data centers is proposed in this paper. First, a working scenario is designed for the enterprise-oriented cloud data centers, including three layers, a user layer, the layer of cloud service provider, and the star layer of cloud data centers. Specifically, the user layer is used to generate the request sets of the virtual machines, and the layer of cloud service provider allocates a large number of request sets of the virtual machines at the user layer to the bottom cloud data center through the classical bin packing algorithm. Then, the mathematical models considering different constraints are established during the allocation of the virtual machines. Finally, the virtual machine allocation among the cloud data centers is optimized using the greedy algorithm. In addition, the big data center of an enterprise is taken as the cloud testing environment, and the test results show that the classical Best-Fit-Algorithm (BFA) performs well in virtual machine allocation and consumes little energy of cloud platforms, providing a reference for the construction of cloud data centers in other enterprises.