With the rise of network virtualization, the workloads deployed on data center are dramatically changed to support diverse service-oriented applications, which are in general characterized by the time-bounded service response that in turn puts great burden on the data-center networks. Although there have been numerous techniques proposed to optimize the virtual network allocation in data center, the research on coordinating them in a flexible and effective way to autonomically adapt to the workloads for service time reduction is few and far between. To address these issues, in this article we propose Sova, an autonomic framework that can combine the virtual dynamic SR-IOV (DSR-IOV) and the virtual machine live migration (VLM) for virtual network allocations in data centers. DSR-IOV is a SR-IOV-based virtual network allocation technology, but its operation scope is very limited to a single physical machine, which could lead to the local hotspot issue in the course of computation and communication, likely increasing the service response time. In contrast, VLM is an often-used virtualization technique to optimize global network traffic via VM migration. Sova exploits the software-defined approach to combine these two technologies with reducing the service response time as a goal. To realize the autonomic coordination, the architecture of Sova is designed based on the MAPE-K loop in autonomic computing. With this design, Sova can adaptively optimize the network allocation between different services by coordinating DSR-IOV and VLM in autonomic way, depending on the resource usages of physical servers and the network characteristics of VMs. To this end, Sova needs to monitor the network traffic as well as the workload characteristics in the cluster, whereby the network properties are derived on the fly to direct the coordination between these two technologies. Our experiments show that Sova can exploit the advantages of both techniques to match and even beat the better performance of each individual technology by adapting to the VM workload changes.

中文翻译：

---

Sova：用于虚拟网络分配的软件定义的自主框架

随着网络虚拟化的兴起，部署在数据中心的工作负载发生了巨大的变化，以支持各种面向服务的应用程序，这些应用程序通常具有限时服务响应的特点，这反过来又给数据中心网络带来了很大的负担。尽管已经提出了许多技术来优化数据中心的虚拟网络分配，但关于以灵活有效的方式协调它们以自动适应工作负载以减少服务时间的研究很少。为了解决这些问题，在本文中，我们提出了 Sova，这是一个自治框架，可以将虚拟动态 SR-IOV (DSR-IOV) 和虚拟机实时迁移 (VLM) 结合起来，用于数据中心的虚拟网络分配。DSR-IOV是一种基于SR-IOV的虚拟网络分配技术，但其运行范围非常局限在单个物理机上，在计算和通信过程中可能会导致本地热点问题，可能会增加服务响应时间。相比之下，VLM 是一种常用的虚拟化技术，通过 VM 迁移优化全球网络流量。Sova 利用软件定义的方法将这两种技术结合起来，以减少服务响应时间为目标。为了实现自主协调，Sova的架构基于自主计算中的MAPE-K循环进行设计。通过这种设计，Sova 可以根据物理服务器的资源使用情况和虚拟机的网络特性，通过自动协调 DSR-IOV 和 VLM 来自适应优化不同服务之间的网络分配。为此，Sova 需要监控网络流量以及集群中的工作负载特征，从而动态派生网络属性以指导这两种技术之间的协调。我们的实验表明，Sova 可以利用这两种技术的优势，通过适应 VM 工作负载变化来匹配甚至击败每种技术的更好性能。