Yeti: Stateless and Generalized Multicast Forwarding
Khaled Diab, Mohamed Hefeeda
摘要
Current multicast forwarding systems suffer from large state requirements at routers and high communication overheads. In addition, these systems do not support generalized multicast forwarding, where traffic needs to pass through trafficengineered paths or requires service chaining. We propose a new system, called Yeti, to efficiently implement generalized multicast forwarding inside ISP networks and supports various forwarding requirements. Yeti completely eliminates the state at routers. Yeti consists of two components: centralized controller and packet processing algorithm. We propose an algorithm for the controller to create labels that represent generalized multicast graphs. The controller instructs an ingress router to attach the created labels to packets in the multicast session. We propose an efficient packet processing algorithm at routers to process labels of incoming packets and forwards them accordingly. We prove the correctness and efficiency of Yeti. In addition, we assess the performance of Yeti in a hardware testbed and using simulations. Our experimental results show that Yeti can efficiently support high speed links. Furthermore, we compare Yeti using real ISP topologies in simulations against the closest systems in the literature: a rule-based approach (built on top of OpenFlow) and two label-based systems. Our simulation results show substantial improvements compared to these systems. For example, Yeti reduces the label overhead by 65.3%, on average, compared to the closest label-based multicast approach in the literature.
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- Cepheus: Accelerating Datacenter Applications with High-Performance RoCE-Capable MulticastWenxue Li, Junyi Zhang, Yufei Liu, Gaoxiong Zeng 等HPCA 2024 · 被引用 13 次
- Orca: Server-assisted Multicast for Datacenter NetworksKhaled Diab, Parham Yassini, Mohamed HefeedaNSDI 2022 · 被引用 13 次
- Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement LearningLie Lu, Qing Li, Dan Zhao, Yuan Yang 等INFOCOM 2023 · 被引用 7 次
- Accelerating Distributed Graph Learning by Using Collaborative In-Network Multicast and AggregationZhaoyi Li, Jiawei Huang, Yijun Li, Jingling Liu 等USENIX ATC 2025 · 被引用 3 次
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