Khanmirza Hamed
Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
PeerJ Comput Sci. 2022 Feb 8;8:e809. doi: 10.7717/peerj-cs.809. eCollection 2022.
Software-defined networking (SDN) enables fast service innovations through network programmability. In SDN, a logically centralized controller compiles a set of policies into the network-level rules. These rules are inserted in the TCAM memory of SDN-enabled switches enabling high-speed matching and forwarding of packets. Unfortunately, TCAMs are available in limited capacities and fall short of accommodating all intended rules, especially in networks with large distinct flows like datacenters. Rule compression is a technique that reduces the number of rules by aggregating them with some similarity factors. This paper introduces WildMinnie, a new rule compression method that aggregates rules based on their common address non-prefix wildcards derived from a group of rules with the same output port number. We explore rule conflict issues and provide solutions to resolve them. We demonstrate the capability of WildMinnie in various datacenter topologies with traffics having different diversity of source-destination addresses and show that WildMinnie outperforms the best-known compression method by 20%, on average.
软件定义网络（SDN）通过网络可编程性实现快速的服务创新。在SDN中，逻辑集中式控制器将一组策略编译为网络级规则。这些规则被插入到支持SDN的交换机的TCAM内存中，以实现数据包的高速匹配和转发。不幸的是，TCAM的容量有限，无法容纳所有预期的规则，尤其是在像数据中心这样具有大量不同流的网络中。规则压缩是一种通过将规则与一些相似性因素聚合来减少规则数量的技术。本文介绍了WildMinnie，一种新的规则压缩方法，该方法基于从一组具有相同输出端口号的规则中派生的公共地址非前缀通配符来聚合规则。我们探讨了规则冲突问题并提供了解决方案。我们展示了WildMinnie在各种数据中心拓扑结构中的能力，这些拓扑结构中的流量具有不同的源-目的地地址多样性，并表明WildMinnie平均比最著名的压缩方法性能高出20%。
