期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 40, 期 9, 页码 2643-2661出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2022.3191351
关键词
Routing; Prediction algorithms; Graphics processing units; Associative memory; Software engineering; Software algorithms; Production; TCAM; router; AI; prediction
资金
- National Key Research and Development Program of China [2019YFB1802603]
- National Natural Science Foundation of China (NSFC) [62172054, 62072047]
- Key Project of Beijing Natural Science Foundation [M21030]
- Beijing University of Posts and Telecommunications (BUPT) Excellent Ph.D. Students Foundation [CX2021232]
- National Science Foundation of China [62032013, 61872213, 61432009]
- SUTD-ZJU IDEA [SUTD-ZJU (VP) 202102]
- SUTD-ZJU IDEA Seed [SUTD-ZJU(SD) 202101]
- [SUTD SRG-ISTD-2021-165]
This paper proposes a lightweight router design that achieves small storage requirement while retaining the original communication connection performance through AI prediction strategy and block-based insertion strategy.
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.
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