A Fast and Memory-Efficient Trie Structure for Name-Based Packet Forwarding

Ghasemi, Chavoosh; Yousefi, Hamed; Shin, Kang G.; Zhang, Beichuan

doi:10.1109/icnp.2018.00046

Cited by 29 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NDN, in contrast to DNS, aims to develop a new Internet architecture, which can leverage the strengths and counteract the weaknesses of the Internet's current host-based communication architecture [58], [59], [61]. With NDN, emerging patterns in communication can be accommodated.…”

Section: A Existing Approachesmentioning

confidence: 99%

Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

View full text Add to dashboard Cite

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications.

show abstract

Section: A Existing Approachesmentioning

confidence: 99%

Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Although NDN name is defined to be variable length, we can estimate its length from the current dataset. [24] estimates the length of routable name from the trace data of URLs in current Internet. Ac-cording to the datasets of this paper, average number of name components is 4.65 and average component length is 4.56 byte.…”

Section: Analyzing Scalability Andmentioning

confidence: 99%

ICN Performance Enhancing Proxies Intended to Mitigate Performance Degradation in Global Content Delivery

Ueda

Tagami

2021

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

“…The performance of packet forwarding is highly dependent on the speed of the name lookup and is determined by the efficiency of the LPM. For these reasons, many previous studies on packet forwarding in NDN focused on improving the speed of the name lookup by increasing the efficiency of LPM [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Name Lookup In Ndn Packet Forwardingmentioning

confidence: 99%

“…Similarly, a name is hierarchically composed of components in NDN. For this reason, several trie-based name lookup techniques were researched [4][5][6][7][8]. Those techniques primarily focused on reducing the average number of memory accesses per lookup to speed up the name lookup.…”

Section: Introductionmentioning

confidence: 99%

Route Prefix Caching Using Bloom Filters in Named Data Networking

Kim

Shin

2020

Applied Sciences

View full text Add to dashboard Cite

This paper proposes an elaborate route prefix caching scheme for fast packet forwarding in named data networking (NDN) which is a next-generation Internet structure. The name lookup is a crucial function of the NDN router, which delivers a packet based on its name rather than IP address. It carries out a complex process to find the longest matching prefix for the content name. Even the size of a name prefix is variable and unbounded; thus, the name lookup is to be more complicated and time-consuming. The name lookup can be sped up by using route prefix caching, but it may cause a problem when non-leaf prefixes are cached. The proposed prefix caching scheme can cache non-leaf prefixes, as well as leaf prefixes, without incurring any problem. For this purpose, a Bloom filter is kept for each prefix. The Bloom filter, which is widely used for checking membership, is utilized to indicate the branch information of a non-leaf prefix. The experimental result shows that the proposed caching scheme achieves a much higher hit ratio than other caching schemes. Furthermore, how much the parameters of the Bloom filter affect the cache miss count is quantitatively evaluated. The best performance can be achieved with merely 8-bit Bloom filters and two hash functions.

show abstract

A Fast and Memory-Efficient Trie Structure for Name-Based Packet Forwarding

Cited by 29 publications

References 29 publications

Towards Efficient and Intelligent Internet of Things Search Engine

Towards Efficient and Intelligent Internet of Things Search Engine

ICN Performance Enhancing Proxies Intended to Mitigate Performance Degradation in Global Content Delivery

Route Prefix Caching Using Bloom Filters in Named Data Networking

Contact Info

Product

Resources

About