论文标题
有效的数据驱动网络功能
Efficient Data-Driven Network Functions
论文作者
论文摘要
云环境需要动态和自适应网络策略。由于高性能约束，因此首选在虚拟网络函数（VNF）中使用高级学习算法（VNF）中使用启发式方法。本文建议水瓶座被动但有效地收集观察结果，并使机器学习能够收集，推断和提供准确的网络信息，而没有产生其他信号和管理开销。本文说明了水瓶座与流量分类器，自动级别系统以及负载平衡器的使用，并展示了水瓶座内的三种不同机器学习范式的使用，即无需审视，监督和增强学习，以推断网络状态。测试床的评估表明，水瓶座提高了网络状态的可见性，并带来了低空费用的显着性能增长。
Cloud environments require dynamic and adaptive networking policies. It is preferred to use heuristics over advanced learning algorithms in Virtual Network Functions (VNFs) in production becuase of high-performance constraints. This paper proposes Aquarius to passively yet efficiently gather observations and enable the use of machine learning to collect, infer, and supply accurate networking state information-without incurring additional signalling and management overhead. This paper illustrates the use of Aquarius with a traffic classifier, an autoscaling system, and a load balancer-and demonstrates the use of three different machine learning paradigms-unsupervised, supervised, and reinforcement learning, within Aquarius, for inferring network state. Testbed evaluations show that Aquarius increases network state visibility and brings notable performance gains with low overhead.
