Dissertation/Thesis Abstract

Measuring Named Data Networks
by Fan, Chengyu, Ph.D., Colorado State University, 2020, 135; 27955168
Abstract (Summary)

Named Data Networking (NDN) is a promising information-centric networking (ICN) Internet architecture that addresses the content directly rather than addressing servers. NDN provides new features, such as content-centric security, stateful forwarding, and in-network caches, to better satisfy the needs of today's applications. After many years of technological research and experimentation, the community has started to explore the deployment path for NDN.

One NDN deployment challenge is measurement. Unlike IP, which has a suite of measurement approaches and tools, NDN only has a few achievements. NDN routing and forwarding are based on name prefixes that do not refer to individual endpoints. While rich NDN functionalities facilitate data distribution, they also break the traditional end-to-end probing based measurement methods. In this dissertation, we present our work to investigate NDN measurements and fill some research gaps in the field.

Our thesis of this dissertation states that we can capture a substantial amount of useful and actionable measurements of NDN networks from end hosts. We start by comparing IP and NDN to propose a conceptual framework for NDN measurements. We claim that NDN can be seen as a superset of IP. NDN supports similar functionalities provided by IP, but it has unique features to facilitate data retrieval. The framework helps identify that NDN lacks measurements in various aspects. This dissertation focuses on investigating the active measurements from end hosts. We present our studies in two directions to support the thesis statement.

We first present the study to leverage the similarities to replicate IP approaches in NDN networks. We show the first work to measure the NDN-DPDK forwarder, a high-speed NDN forwarder designed and implemented by the National Institute of Standards and Technology (NIST), in a real testbed. The results demonstrate that Data payload sizes dominate the forwarding performance, and efficiently using every fragment to improve the goodput.

We then present the first work to replicate packet dispersion techniques in NDN networks. Based on the findings in the NDN-DPDK forwarder benchmark, we devise the techniques to measure interarrivals for Data packets. The results show that the techniques successfully estimate the capacity on end hosts when 1Gbps network cards are used. Our measurements also indicate the NDN-DPDK forwarder introduces variance in Data packet interarrivals. We identify the potential bottlenecks and the possible causes of the variance.

We then address the NDN specific measurements, measuring the caching state in NDN networks from end hosts. We propose a novel method to extract fingerprints for various caching decision mechanisms. Our simulation results demonstrate that the method can detect caching decisions in a few rounds. We also show that the method is not sensitive to cross-traffic and can be deployed on real topologies for caching policy detection.

Indexing (document details)
Advisor: Partridge, Craig, Papadopoulos, Christos
Commitee: Pallickara, Shrideep, Pallickara, Sangmi, Luo, J. Rockey
School: Colorado State University
Department: Computer Science
School Location: United States -- Colorado
Source: DAI-B 81/12(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer science
Keywords: Caching, Information Centric Networking, Named Data Networking, Network measurement, Packet dispersion techniques
Publication Number: 27955168
ISBN: 9798635246245
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