1,070 research outputs found
Content Delivery Latency of Caching Strategies for Information-Centric IoT
In-network caching is a central aspect of Information-Centric Networking
(ICN). It enables the rapid distribution of content across the network,
alleviating strain on content producers and reducing content delivery
latencies. ICN has emerged as a promising candidate for use in the Internet of
Things (IoT). However, IoT devices operate under severe constraints, most
notably limited memory. This means that nodes cannot indiscriminately cache all
content; instead, there is a need for a caching strategy that decides what
content to cache. Furthermore, many applications in the IoT space are
timesensitive; therefore, finding a caching strategy that minimises the latency
between content request and delivery is desirable. In this paper, we evaluate a
number of ICN caching strategies in regards to latency and hop count reduction
using IoT devices in a physical testbed. We find that the topology of the
network, and thus the routing algorithm used to generate forwarding
information, has a significant impact on the performance of a given caching
strategy. To the best of our knowledge, this is the first study that focuses on
latency effects in ICN-IoT caching while using real IoT hardware, and the first
to explicitly discuss the link between routing algorithm, network topology, and
caching effects.Comment: 10 pages, 9 figures, journal pape
Jointly Optimal Routing and Caching for Arbitrary Network Topologies
We study a problem of fundamental importance to ICNs, namely, minimizing
routing costs by jointly optimizing caching and routing decisions over an
arbitrary network topology. We consider both source routing and hop-by-hop
routing settings. The respective offline problems are NP-hard. Nevertheless, we
show that there exist polynomial time approximation algorithms producing
solutions within a constant approximation from the optimal. We also produce
distributed, adaptive algorithms with the same approximation guarantees. We
simulate our adaptive algorithms over a broad array of different topologies.
Our algorithms reduce routing costs by several orders of magnitude compared to
prior art, including algorithms optimizing caching under fixed routing.Comment: This is the extended version of the paper "Jointly Optimal Routing
and Caching for Arbitrary Network Topologies", appearing in the 4th ACM
Conference on Information-Centric Networking (ICN 2017), Berlin, Sep. 26-28,
201
Measuring named data networks
2020 Spring.Includes bibliographical references.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
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