High-performance IPv6 address lookup in GPU-accelerated software routers

Lin, Feng; Wang, Gang; Zhou, Junhai; Zhang, Shiwen; Yao, Xin

doi:10.1016/j.jnca.2016.08.004

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…Bajpai and Schönwälder [23] found that TCP connection establishment times of IPv6 are relatively higher than that of IPv4 in the cases when CDN caches are present for IPv4, but are largely absent for IPv6. Lin et al [24] designed and implemented a high-performance IPv6 address lookup engine in GPU-accelerated software routers. In this paper, we revisit several critical performance metrics of IPv6 network, including reachability, delay, and packetloss rate.…”

Section: Related Workmentioning

confidence: 99%

A Case Study of IPv6 Network Performance: Packet Delay, Loss, and Reordering

Wang

Pan

et al. 2017

Mathematical Problems in Engineering

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Internet Protocol (IP) is used to identify and locate computers on the Internet. Currently, IPv4 still routes most Internet traffic. However, with the exhausting of IPv4 addresses, the transition to IPv6 is imminent, because, as the successor of IPv4, IPv6 can provide a larger available address space. Existing studies have addressed the notion that IPv6-centric next generation networks are widely deployed and applied. In order to gain a deep understanding of IPv6, this paper revisits several critical IPv6 performance metrics. Our extensive measurement shows that packet delay and loss rate of IPv6 are similar to IPv4 when the AS-level paths are roughly the same. Specifically, when the link utilization exceeds a threshold, for example, 0.83 in our study, variation of packet delay presents a similar pattern with the variation of link utilization. If packet delay of a path is large, packet-loss rate of that path is more likely to fluctuate. In addition, we conduct a first-ever analysis of packet reordering in IPv6 world. Few IPv6 probe packets are out-of-order and the reordering rate is 2.3 * 10 −6 , which is much lower than that of 0.79% in IPv4 world. Our analysis consolidates an experimental basis for operators and researchers of IPv6 networks.

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Section: Related Workmentioning

confidence: 99%

A Case Study of IPv6 Network Performance: Packet Delay, Loss, and Reordering

Wang

Pan

et al. 2017

Mathematical Problems in Engineering

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“…Several investigations including (Li et al 2013;Lin et al 2016) have attempted to study the use of CUDA platform for parallel implementation of network functions such as IP lookup in routing tables, aiming at having access to higher throughput. This tool has been used for executing parallel genetic algorithms (Zhao et al 2018), neural networks (Gong et al 2017) and ant colony optimization algorithm (Llanes et al 2016).…”

Section: Graphics Processing Unitmentioning

confidence: 99%

Peer Review #1 of "Enhancing the performance of the aggregated bit vector algorithm in network packet classification using GPU (v0.1)"

2019

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Packet classification is a computationally intensive, highly parallelizable task in many advanced network systems like high-speed routers and firewalls that enable different functionalities through discriminating incoming traffic. Recently, graphics processing units (GPUs) have been exploited as efficient accelerators for parallel implementation of software classifiers. The Aggregated bit vector (ABV) is a highly parallelizable packet classification algorithm. In this work, first we present a parallel kernel for running this algorithm on GPUs. Next we adapt an asymptotic analysis method which predicts any empirical result of the proposed kernel. Experimental results not only confirm the efficiency of the proposed parallel kernel but also reveal the accuracy of the analysis method in predicting important trends in experimental results.

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“…If a system can tolerate negligible overhead, then Bloom Filter can enhance a performance of a system. Therefore, Bloom Filter is deployed to various domains, namely, Big Data [9], Deduplication [10], [11], [12], Network Security [4], [13], Network Traffic control [14], Name Lookup [15], [16], IP address lookup [17], [18], Biometric [19], [20], Bioinformatics [21], [22], File System [23], Indexing, and many more. However, Bloom Filter is not suitable in case of correct query-answer requirements.…”

Section: Bloom Filtermentioning

confidence: 99%

Role of Bloom Filter in Big Data Research: A Survey

Patgiri¹,

Nayak²,

Borgohain³

2018

ijacsa

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Big Data is the most popular emerging trends that becomes a blessing for human kinds and it is the necessity of day-to-day life. For example, Facebook. Every person involves with producing data either directly or indirectly. Thus, Big Data is a high volume of data with exponential growth rate that consists of a variety of data. Big Data touches all fields, including Government sector, IT industry, Business, Economy, Engineering, Bioinformatics, and other basic sciences. Thus, Big Data forms a data silo. Most of the data are duplicates and unstructured.To deal with such kind of data silo, Bloom Filter is a precious resource to filter out the duplicate data. Also, Bloom Filter is inevitable in a Big Data storage system to optimize the memory consumption. Undoubtedly, Bloom Filter uses a tiny amount of memory space to filter a very large data size and it stores information of a large set of data. However, functionality of the Bloom Filter is limited to membership filter, but it can be adapted in various applications. Besides, the Bloom Filter is deployed in diverse field, and also used in the interdisciplinary research area. Bioinformatics, for instance. In this article, we expose the usefulness of Bloom Filter in Big Data research.

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High-performance IPv6 address lookup in GPU-accelerated software routers

Cited by 16 publications

References 24 publications

A Case Study of IPv6 Network Performance: Packet Delay, Loss, and Reordering

A Case Study of IPv6 Network Performance: Packet Delay, Loss, and Reordering

Peer Review #1 of "Enhancing the performance of the aggregated bit vector algorithm in network packet classification using GPU (v0.1)"

Role of Bloom Filter in Big Data Research: A Survey

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