Named Data Networking (NDN) is a promising Internet architecture that aims to supersede the current IP-based architecture and shift the host-centric model to a data-centric one. Within NDN, forwarding Interest packets remains a significant challenge and has attracted considerable recent research attention. The momentum behind machine learning techniques, especially reinforcement learning, is steadily growing, offering the potential to deliver intelligent, adaptable, and reliable NDN forwarding algorithms. In this context, this paper proposes efficient NDN forwarding strategies based on Contextual Multi-Armed Bandit (CMAB). Initially, we employ CMAB to address the challenge of forwarding Interest packets and introduce a new CMAB model tailored for NDN, dubbed CMAB4NDN. Subsequently, we construct the CMAB context using information derived from the content name and the network congestion state, which are then fed into the CMAB4NDN learning algorithm to decide on the best forwarding action. Further, we develop three CMAB strategies, namely Lin-ɛ-Greedy, Linear Upper Confidence Bound, and Contextual Thompson Sampling, and deploy them within our proposal. CMAB4NDN was implemented in ndnSIM, thoroughly evaluated, and compared with multiple state-of-the-art NDN forwarding algorithms across various scenarios. The obtained results confirm the relevance and superiority of our approach in terms of delay, throughput, and packet loss.

中文翻译：

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用于 NDN 转发的上下文 Multi-Armed Bandit 方法


命名数据网络（NDN）是一种很有前景的互联网架构，旨在取代当前基于IP的架构，并将以主机为中心的模型转变为以数据为中心的模型。在 NDN 中，转发兴趣包仍然是一个重大挑战，并且最近引起了相当多的研究关注。机器学习技术（尤其是强化学习）背后的动力正在稳步增长，为提供智能、适应性强且可靠的 NDN 转发算法提供了潜力。在此背景下，本文提出了基于上下文多臂强盗（CMAB）的高效NDN转发策略。最初，我们使用 CMAB 来解决转发兴趣包的挑战，并引入了一种专为 NDN 量身定制的新 CMAB 模型，称为 CMAB4NDN。随后，我们使用从内容名称和网络拥塞状态导出的信息构建 CMAB 上下文，然后将其输入 CMAB4NDN 学习算法以决定最佳转发操作。此外，我们开发了三种 CMAB 策略，即 Lin-ɛ-Greedy、线性上置信界和上下文汤普森采样，并将它们部署在我们的提案中。 CMAB4NDN 在 ndnSIM 中实现，经过全面评估，并与各种场景下的多种最先进的 NDN 转发算法进行比较。获得的结果证实了我们的方法在延迟、吞吐量和丢包方面的相关性和优越性。