Named Data Networking (NDN) is a promising approach to provide fast in-network access to compact muon solenoid (CMS) datasets. It proposes a content-centric rather than a host-centric approach to data retrieval. Data packets with unique and immutable names are retrieved from a content store (CS) using Interest packets. The current NDN architecture relies on forwarding strategies that are only dependent upon on-path caching. Such a design does not take advantage of the cached content available on the adjacent off-path routers in the network, thus reducing data transfer efficiency. In this work, we propose a software-defined, storage-aware routing mechanism that leverages NDN router cache-states, software defined networking (SDN) and multipath forwarding strategies to improve the efficiency of very large data transfers. First, we propose a novel distributed multipath (D-MP) forwarding strategy and enhancements to the NDN Interest forwarding pipeline. In addition, we develop a centralized SDN-enabled control for the multipath forwarding strategy (S-MP), which leverages the global knowledge of NDN network states that distributes Interests efficiently. We perform extensive evaluations of our proposed methods on an at-scale wide area network (WAN) testbed spanning six geographically separated sites. Our proposed solutions easily outperform the existing NDN forwarding strategies. The D-MP strategy results in performance gains ranging between 10.4x to 12.5x over the default NDN implementation without in-network caching, and 12.2x to 18.4x with in-network caching enabled. For S-MP strategy, we demonstrate a performance improvement of 10.6x to 12.6x, and 12.9x to 18.5x, with in-network caching disabled and enabled, respectively. Further, we also present a comprehensive analysis of NDN cache management for large data transfers and propose a novel prefetching mechanism to improve data transfer performance. Due to the inherent capacity limitations of the NDN router caches, we use SDN to provide an intelligent and efficient solution for data distribution and routing across multiple NDN router caches. We demonstrate how software-defined control can be used to partition and distribute large CMS files based on NDN router cache-state knowledge. Further, SDN control will also configure the router forwarding strategy to retrieve CMS data from the network. Our proposed solution demonstrates that the CMS datasets can be retrieved 28%–38% faster from the NDN routers’ caches than existing NDN approaches. Lastly, we develop a prefetching mechanism to improve the transfer performance of files that are not available in the router’s cache.

命名数据网络 (NDN) 是一种很有前途的方法，可以提供对紧凑μ子螺线管 (CMS) 数据集的快速网络内访问。它提出了一种以内容为中心而非以主机为中心的数据检索方法。数据具有独特和不可变名包从使用内容存储器（CS）获取利息包。当前的 NDN 架构依赖于仅依赖于路径缓存的转发策略。这种设计没有利用相邻路径外可用的缓存内容网络中的路由器，从而降低数据传输效率。在这项工作中，我们提出了一种软件定义的、存储感知的路由机制，它利用 NDN 路由器缓存状态、软件定义网络 (SDN) 和多路径转发策略来提高超大数据传输的效率。首先，我们提出了一种新颖的分布式多路径(D-MP) 转发策略和对 NDN 兴趣转发管道的增强。此外，我们为多路径转发策略 (S-MP)开发了一个支持SDN的集中控制，它利用了有效分配兴趣的 NDN 网络状态的全局知识。我们上执行我们提出的方法进行广泛评估的尺度跨越六个地理上分离的站点的广域网 (WAN) 测试平台。我们提出的解决方案很容易胜过现有的 NDN 转发策略。D-MP 策略的性能提升比默认 NDN 实现高 10.4 到 12.5 倍，没有网络内缓存，12.2 到 18.4 倍，启用网络缓存。对于 S-MP 策略，我们展示了 10.6 倍到 12.6 倍和 12.9 倍到 18.5 倍的性能提升，网络内缓存分别禁用和启用。此外，我们还对大数据传输的 NDN 缓存管理进行了全面分析，并提出了一种新颖的预取机制来提高数据传输性能。由于 NDN 路由器缓存的固有容量限制，我们使用 SDN 为跨多个 NDN 路由器缓存的数据分发和路由提供了智能高效的解决方案。我们演示了如何使用软件定义的控制来根据 NDN 路由器缓存状态知识对大型 CMS 文件进行分区和分发。此外，SDN 控制还将配置路由器转发策略以从网络检索 CMS 数据。我们提出的解决方案表明，从 NDN 路由器的缓存中检索 CMS 数据集的速度比现有的 NDN 方法快 28%–38%。最后，