On broadcast-based self-learning in named data networking
On broadcast-based self-learning in named data networking
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DOI：
10.23919/ifipnetworking.2017.8264832
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Junxiao Shi;Eric Newberry;Beichuan Zhang
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文献类型：
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作者：
Junxiao Shi;Eric Newberry;Beichuan Zhang
在局域网和移动自组织网络中，基于广播的自学习是一种常见的寻找数据包传递路径的机制。自学习广播第一个数据包，观察返回数据包的来源，然后创建相应的转发表条目，以便以后的数据包只需要单播。这种机制的主要优点是简单、适应性强和支持移动性。虽然基于广播的自学习的高级思想很简单，但要使该方案高效和安全，特别是在命名数据网络（NDN）等以数据为中心的网络体系结构中，需要仔细检查。本文研究了基于广播的自学习如何应用于NDN网络，指出了两个主要问题：名称前缀粒度问题和信任问题，并提出了相应的解决方案。我们还将自学习应用于交换以太网作为示例，以开发一种特定的设计，该设计可以在没有任何控制协议的情况下构建转发表，从链路故障中快速恢复，并利用离路缓存。利用真实流量和合成流量进行了仿真，以评估设计的性能。
In local area networks and mobile ad-hoc networks, broadcast-based self-learning is a common mechanism to find packet delivery paths. Self-learning broadcasts the first packet, observes where the returning packet comes from, then creates the corresponding forwarding table entry so that future packets will only need unicast. The main benefits of this mechanism are its simplicity, adaptability, and support of mobility. While the high-level idea of broadcast-based self-learning is straightforward, making the scheme efficient and secure, especially in a data-centric network architecture like Named Data Networking (NDN), requires careful examination. In this paper, we study how broadcast-based self-learning may be applied to NDN networks, point out two major issues: the name-prefix granularity problem and the trust problem, and propose corresponding solutions. We also apply self-learning to switched Ethernet as an example to develop a specific design that can build forwarding tables without any control protocol, recover quickly from link failures, and make use of off-path caches. Simulations are conducted using both real and synthetic traffic to evaluate the performance of the design.