As greater numbers of data-intensive applications are required to process big data in bandwidth-constrained datacenters with heterogeneous physical machines (PMs) and virtual machines (VMs), network core traffic is experiencing rapid growth. The VMs of a virtual cluster (VC) must be allocated as compactly as possible to avoid bandwidth-related bottlenecks. Since each PM/switch has a certain failure probability, a VC may not be executed when it meets with any PM/switch fault. Although the VMs of a VC can be spread out across different fault domains to minimize the risk of violating the availability requirement of the VC, this increases the network core traffic. Therefore, avoiding the decrease in availability caused by the heterogeneous PM/switch failure probabilities and bandwidth-related bottlenecks has been a constant challenge. In this paper, we first introduce a joint optimization function to measure the overall risk cost and overall bandwidth usage in the network core to allocate the same set of data-intensive applications. We then introduce an approach to maximize the value of the joint optimization function. Finally, we performed a side-by-side comparison with prior algorithms, and the experimental results show that our approach outperforms the other existing algorithms.

中文翻译：

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带宽受限数据中心中的可用性感知虚拟集群分配

由于需要更多数据密集型应用程序来处理具有异构物理机 (PM) 和虚拟机 (VM) 的带宽受限数据中心中的大数据，因此网络核心流量正在快速增长。虚拟集群 (VC) 的 VM 必须尽可能紧凑地分配，以避免与带宽相关的瓶颈。由于每个PM/switch 都有一定的故障概率，当遇到任何PM/switch 故障时，VC 可能不会被执行。尽管 VC 的 VM 可以分布在不同的故障域中以最大程度地降低违反 VC 可用性要求的风险，但这会增加网络核心流量。因此，避免由异构 PM/交换机故障概率和带宽相关瓶颈引起的可用性下降一直是一个挑战。在本文中，我们首先引入联合优化功能来衡量网络核心中的整体风险成本和整体带宽使用情况，以分配相同的数据密集型应用程序集。然后我们介绍了一种最大化联合优化函数值的方法。最后，我们与现有算法进行了并排比较，实验结果表明我们的方法优于其他现有算法。