Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JSAC.2022.3191351 | IEEE Journal on Selected Areas in Communications |
Keywords | DocType | Volume |
TCAM,router,AI,prediction | Journal | 40 |
Issue | ISSN | Citations |
9 | 0733-8716 | 0 |
PageRank | References | Authors |
0.34 | 42 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peizhuang Cong | 1 | 3 | 2.74 |
| Yuchao Zhang | 2 | 56 | 12.88 |
| Bin Liu | 3 | 1599 | 161.90 |
| Wendong Wang | 4 | 821 | 72.69 |
| Zehui Xiong | 5 | 586 | 54.94 |
| Ke Xu | 6 | 1392 | 171.73 |