Hydra：利用功能切片实现高效的分布式SDN控制器
Hydra: Leveraging Functional Slicing for Efficient Distributed SDN Controllers
摘要：
当今扩展软件定义网络（SDN）控制器的传统方法是基于网络拓扑来划分交换机，其中每个分区由单个物理控制器控制，运行所有SDN应用。然而，拓扑划分受到以下事实的限制：（i）延迟敏感（例如，监视）与给定分区相关联的SDN应用可能受到协同定位的计算密集型（例如，路由计算）应用;（ii）同时实现低收敛时间和响应时间可能是具有挑战性的;以及（iii）跨分区的应用程序实例之间的通信可能增加延迟。为了解决这些问题，在本文中，我们探索功能切片，一种扩展的补充方法，其中属于同一拓扑分区的多个SDN应用程序可以放置在物理上不同的服务器中。我们提出了Hydra，一个基于功能切片的分布式SDN控制器框架。Hydra基于收敛时间选择分区作为主要度量标准，但是在考虑分区的应用程序之间的通信以及分区之间的应用程序实例的通信时，以保持较低的响应时间的方式跨分区放置应用程序实例。使用Floodlight控制器进行的评估显示了Hydra在保持故障收敛时间较小的同时，保持每个分区更高的吞吐量并确保对延迟敏感的应用程序的响应能力方面的重要性和有效性。
英文摘要：
The conventional approach to scaling Software Defined Networking (SDN) controllers today is to partition switches based on network topology, with each partition being controlled by a single physical controller, running all SDN applications. However, topological partitioning is limited by the fact that (i) performance of latency-sensitive (e.g., monitoring) SDN applications associated with a given partition may be impacted by co-located compute-intensive (e.g., route computation) applications; (ii) simultaneously achieving low convergence time and response times might be challenging; and (iii) communication between instances of an application across partitions may increase latencies. To tackle these issues, in this paper, we explore functional slicing, a complementary approach to scaling, where multiple SDN applications belonging to the same topological partition may be placed in physically distinct servers. We present Hydra, a framework for distributed SDN controllers based on functional slicing. Hydra chooses partitions based on convergence time as the primary metric, but places application instances across partitions in a manner that keeps response times low while considering communication between applications of a partition, and instances of an application across partitions. Evaluations using the Floodlight controller show the importance and effectiveness of Hydra in simultaneously keeping convergence times on failures small, while sustaining higher throughput per partition and ensuring responsiveness to latency-sensitive applications.
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