With the ever-increasing deployment of 5G and IoT, the number of end-hosts/terminals is increasing rapidly, so that routers have to cache more and more forwarding entries to guarantee communication reachability of these terminals, which makes Ternary Content Addressable Memory (TCAM)-based routers keep expanding resource requirements. However, the design and implementation of large-capacity TCAM-based routers are faced with such challenges: difficult circuit design, high production cost and energy consumption, thereby posing an urgent requirement on a lightweight TCAM that can still maintain those massive communication connections. In this paper, we aim to design a lightweight router with small storage requirement while still retaining the original communication connection performance, which is not straightforward due to the following two challenges: First, under the condition of massive sequential flow data, it’s difficult to accurately and timely select the entries to cache for a small capacity TCAM. Second, given the strict prefix matching principle, how to efficiently insert the selected entries into TCAM is also challenging. To address these problems, we propose A&B: an AI-based Routing entry prediction strategy (AIR) and a Block-based entry Insertion Tactic (BIT). AIR can precisely select entries by conducting accurate entry predictions, which converts dynamic flow-based prediction into stable and parallelizable entry-based prediction by decoupling spatio-temporal characteristics. BIT optimizes entry insertion by isolating TCAM into several blocks, thus eliminating the time-consuming entry movements. The experiment results based on real backbone traffic show that our lightweight A&B achieves comparable performance compared to the traditional schemes by using only 1/8 TCAM storage.

中文翻译：

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A&B：路由器的AI和基于块的TCAM条目替换方案


随着5G和物联网的不断部署，终端主机/终端的数量迅速增加，路由器必须缓存越来越多的转发表项来保证这些终端的通信可达性，这使得三态内容寻址存储器（TCAM）基于 ) 的路由器不断扩大资源需求。然而，基于大容量TCAM的路由器的设计和实现面临着电路设计困难、生产成本高和能耗高等挑战，从而迫切需要一种轻量级的TCAM，但仍能维持如此海量的通信连接。在本文中，我们的目标是设计一种存储需求较小的轻量级路由器，同时仍保留原有的通信连接性能，这并不简单，因为存在以下两个挑战：首先，在海量顺序流数据的情况下，很难准确地对于小容量的TCAM，及时选择条目进行缓存。其次，考虑到严格的前缀匹配原则，如何将选定的条目高效地插入到TCAM中也具有挑战性。为了解决这些问题，我们提出了 A&B：基于 AI 的路由条目预测策略（AIR）和基于块的条目插入策略（BIT）。 AIR可以通过准确的条目预测来精确选择条目，通过时空特性解耦，将动态的基于流的预测转换为稳定且可并行的基于条目的预测。 BIT 通过将 TCAM 隔离到多个块来优化条目插入，从而消除耗时的条目移动。 基于真实骨干流量的实验结果表明，我们的轻量级A&B仅使用1/8的TCAM存储就达到了与传统方案相当的性能。