分布式SDN控制平面中RAFT一致性的响应时间和可用性研究
Response Time and Availability Study of RAFT Consensus in Distributed SDN Control Plane
摘要：
软件定义网络（SDN）承诺前所未有的灵活性和网络操作的简易性。虽然灵活性是利用新技术优势的重要因素，但关键基础设施网络对网络鲁棒性和控制平面延迟也有严格的要求。SDN控制平面中的鲁棒性通过部署多个分布式控制器来实现，这些控制器形成集群以实现耐久性和快速故障转移的目的。然而，在目前的文献中，控制器聚类对整个系统响应时间的影响没有得到很好的研究。因此，在这项工作中，我们提供了一个详细的分析研究的分布式共识算法RAFT，在OpenDaylight和ONOS SDN控制器平台上实现。在这些控制器中，RAFT实现了数据存储复制，控制器故障后的领导者选举和成功修复的控制器状态恢复。为了评估其性能，我们介绍了一个框架，数值分析的各种SDN集群组织w.r.t.它们的响应时间和可用性度量。我们使用随机活动网络建模的RAFT操作，故障注入和集群恢复过程中，并使用真实世界的实验，我们收集的速率参数提供现实的输入，一个有代表性的集群恢复模型。我们还展示了如何一个快速的复兴机制，用于治疗软件错误引起的故障，可以最大限度地减少总响应时间所经历的控制器客户端，同时保证更高的系统可用性在长期。
英文摘要：
Software defined networking (SDN) promises unprecedented flexibility and ease of network operations. While flexibility is an important factor when leveraging advantages of a new technology, critical infrastructure networks also have stringent requirements on network robustness and control plane delays. Robustness in the SDN control plane is realized by deploying multiple distributed controllers, formed into clusters for durability and fast-failover purposes. However, the effect of the controller clustering on the total system response time is not well investigated in current literature. Hence, in this work we provide a detailed analytical study of the distributed consensus algorithm RAFT, implemented in OpenDaylight and ONOS SDN controller platforms. In those controllers, RAFT implements the data-store replication, leader election after controller failures and controller state recovery on successful repairs. To evaluate its performance, we introduce a framework for numerical analysis of various SDN cluster organizations w.r.t. their response time and availability metrics. We use Stochastic Activity Networks for modeling the RAFT operations, failure injection and cluster recovery processes, and using real-world experiments, we collect the rate parameters to provide realistic inputs for a representative cluster recovery model. We also show how a fast rejuvenation mechanism for the treatment of failures induced by software errors can minimize the total response time experienced by the controller clients, while guaranteeing a higher system availability in the long-term.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
