With the explosive growth in the size of datasets in cloud applications, the demands for network bandwidth within a data center are increasing tremendously. Applications running in cloud data centers are commonly composed of clusters of virtual machines (VMs) that communicate extensively with each other, resulting in increased pressure on network bandwidth.Server consolidation exacerbates the problem by placing multiple VMs from possibly different applications on a small set of physical machines and multiplexing server resources among them. Network-aware virtual machine placement (NAVMP) aims to place the VMs in a virtual cluster on the physical servers (hosts) of a data center to minimize the communication bottleneck. The problem is NP-hard and no existing exact method is able to scale up satisfactorily.

In this thesis, we propose a hybrid genetic algorithm to solve the NAVMP problem. We utilize a two-stage approach made up of a greedy heuristic to find a set of good initial solutions that serve as seeds for a genetic algorithm to improve the quality of the solutions. The algorithm tends to place VMs that exchange a large amount of data on the same host if possible, and to align the virtual machine cluster communications and physical machine topology in the training process. Simulation results show that our algorithm can benefit both traffic flow and load balance in the routers.