Fast Regular Expression Matching Using Small TCAM

Meiners, Chad R.; Patel, Jignesh; Norige, Eric; Liu, Alex X.; Torng, Eric

doi:10.1109/tnet.2013.2256466

Cited by 31 publications

(11 citation statements)

References 25 publications

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“…Our data harvesting process involved data discovery, data extraction, and database management [38]. The main techniques that were used include the Web, breadth-first search, regular expressions, multithreading, and database technologies [39,40]. Our procedure of the data harvesting includes getting the URL, retrieving source code, analyzing hotel's website and retrieving sources codes, retrieving related data, processing data, and storing data in a database.…”

Section: Data Harvestingmentioning

confidence: 99%

Synthesizing Data to Explore the Dynamic Spatial Patterns of Hotel Development

Yin

Cole

et al. 2019

IJGI

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The spatio-temporal relationship between tourism product similarity and spatial proximity has not been adequately studied empirically because of data and methodological limitations. New forms of data available at high temporal frequencies and low levels of spatial aggregation, together with large commercial data and expanding computational ability allow a variety of theories, old and new to be explored and evaluated more meticulously and systemically than has been possible hitherto. This study uses spatial visualization and data harvesting to synthesize a variety of data for exploring the evolution of hotel clusters and co-location synergies in US cities. The findings question the reliability of the current data to be used for identifying and analyzing the formation of tourist destination clusters and their dynamics. We conclude that synthesizing social media and large commercial data can generate a more robust database for research on tourism development and planning and improving opportunities for the examining spatial patterns of tourism activities. We also devise a protocol to combine ‘social media’ sources with big commercial sources for tourism development and planning, and eventually other sectors.

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Section: Data Harvestingmentioning

confidence: 99%

Synthesizing Data to Explore the Dynamic Spatial Patterns of Hotel Development

Yin

Cole

et al. 2019

IJGI

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“…However, based on the high cost of hardware, the TCAM is more expensive and the storage capacity is limited [14]. At the same time, due to the algorithm is based on the hardware, so the scalability is poor [15].…”

Section: Related Algorithms and Researchmentioning

confidence: 99%

A Multi-stage IPv6 Routing Lookup Algorithm Based on Hash Table and Multibit Trie

He¹,

Yang²

2017

dtcse

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The address length of IPv6 is 128 and it has no classification addressing, which makes routing lookup processing burden of core router is heavier and the requirements are higher. As many existing algorithms cannot adapt to new requirements after expanding to IPv6, it is necessary to propose a new routing lookup algorithm based on IPv6. After thorough analysis of the IPv6 address prefix distribution regularity, this paper proposes a new scheme based on simple data structure: Hash Table and Multibit Trie (retrieval). This algorithm first classifies the prefix value and then starts its first lookup by using the first 48bits prefix of the IPv6 Address. Simulation results show that in most cases, the algorithm needs only one memory access to find the next hop routing information. This algorithm has high lookup speed and can be easily implemented with hardware.

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“…Current recovery schemes including proactive‐ and reactive‐based recovery ones may be difficult to satisfy the increasing demands during the recovery due to ignoring the trade‐off between resource utilization and recovery time. On the one hand, the proactive recovery scheme needs additional Ternary Content Addressable Memory (TCAM) resources for achieving local recovery. Since an SDN switch can only support limited rules in its TCAM, the storage resources reserved for backup paths will be wasted if there is not any failure.…”

Section: Introductionmentioning

confidence: 99%

CFR: A cooperative link failure recovery scheme in software‐defined networks

Wang

Yao

et al. 2018

Int J Communication

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Summary Software‐defined networking that separates the control plane from the data plane is envisioned as a promising technology to enable resilient and flexible network management. Tolerating link failures is a fundamental problem in enhancing such network resilience in software‐defined networking. Reactive and proactive fault tolerant schemes for conventional networks may not well balance the fault recovery time and network performance, since the proactive scheme typically underutilizes resources and the reactive scheme usually incurs a longer recovery time. In this paper, we propose a cooperative link failure recovery scheme to find a fine‐grained trade‐off between resource utilization and recovery time by combining reactive and proactive methods. We formalize the problem of link failure recovery as a multiobjective optimization problem and devise a 2‐stage algorithm for it. The first stage of the algorithm guarantees connectivity restoration in an acceptable recovery interval based on fast failover feature supported in OpenFlow protocol, meanwhile it assigns virtual local area network tags to back up paths for achieving a lower memory consumption. The second stage of the algorithm guarantees the quality of service for different applications by adjusting the backup paths after rapid connectivity restoration. Extensive simulations highlight that cooperative link failure recovery scheme can satisfy both the carrier‐grade recovery requirements and quality of service requirements in terms of delay and network bandwidth.

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Fast Regular Expression Matching Using Small TCAM

Cited by 31 publications

References 25 publications

Synthesizing Data to Explore the Dynamic Spatial Patterns of Hotel Development

Synthesizing Data to Explore the Dynamic Spatial Patterns of Hotel Development

A Multi-stage IPv6 Routing Lookup Algorithm Based on Hash Table and Multibit Trie

CFR: A cooperative link failure recovery scheme in software‐defined networks

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