Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement AppBag, an Application-aware Bandwidth guarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that AppBag can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.

中文翻译：

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通过提前一步的流量信息促进云中应用感知的带宽分配

虚拟机 (VM) 的带宽分配对托管在 VM 中的通信密集型大数据应用程序的性能有重大影响。准确确定要为 VM 保留多少带宽以及何时对其进行调整至关重要。过去的方法通常采用预测应用程序对带宽分配的长期网络需求。然而，这些方法缺乏预测精度，导致应用程序性能不可预测。最近，人们承认应用程序的网络需求只能在它们的每个执行阶段之前准确地得出。因此，及时为信息有限的虚拟机分配带宽是一项挑战。在本文中，我们设计并实现了应用感知带宽保证框架 AppBag，它将准确的带宽分配给具有提前一步流量信息的虚拟机。我们提出了一种算法来为 VM 分配带宽并将它们映射到可行的主机上。为了减少调整分配时的开销，提出了一种高效的、性能有界的延迟迁移（LM）算法。我们使用真实世界的应用程序进行了广泛的评估，结果表明 AppBag 可以在运行时处理带宽请求，同时将应用程序的执行时间减少 47.3%，将全局流量减少 36.7%，与最新状态相比——艺术方法。