As more and more services are deployed in the cloud datacenter, network traffic is growing exponentially. Virtual machines (VMs) of a virtual cluster (VC) must be allocated on physical machines (PMs) in the datacenter with a certain topology. Each VM needs some resources to run various services. Apparently, allocating VMs in the datacenter as compactly as possible can reduce traffic consumption and avoid bandwidth-related bottlenecks. However, loose allocation scheme can reduce the loss expectation of VMs due to failure possibilities of PMs and switches, and the availability of the VC is increased thereby. To enhance availability and reduce network bandwidth usage, it is significant to determine the scheme of allocating VMs of the VC. In this paper, we first introduce four typical datacenter architectures with network topologies and corresponding cost matrices, and extend to generality. Then we propose a joint optimization function to measure the risk of VC and the core bandwidth usage with a global availability constraint. Subsequently, an evolutionary algorithm is raised to minimize the value of the constrained optimization function. Finally, the evaluation results show the effectiveness of the proposed approach and performance advancement over the existing approaches.

中文翻译：

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云数据中心虚拟集群分配的联合可用性增强和流量优化

随着越来越多的服务部署在云数据中心，网络流量呈指数级增长。虚拟集群 (VC) 的虚拟机 (VM) 必须分配在具有特定拓扑结构的数据中心的物理机 (PM) 上。每个 VM 都需要一些资源来运行各种服务。显然，在数据中心尽可能紧凑地分配虚拟机可以减少流量消耗并避免与带宽相关的瓶颈。然而，松散分配方案可以降低由于PM和交换机故障的可能性而导致的VM的损失预期，从而增加了VC的可用性。为了提高可用性并减少网络带宽使用，确定VC的VM分配方案很重要。在本文中，我们首先介绍了四种典型的数据中心架构以及网络拓扑结构和相应的成本矩阵，并扩展到一般性。然后我们提出了一个联合优化函数来衡量具有全局可用性约束的 VC 和核心带宽使用的风险。随后，提出了一种进化算法来最小化约束优化函数的值。最后，评估结果显示了所提出方法的有效性和对现有方法的性能提升。