Extending the NFComms: framework for bulk data transfers
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2009
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/430164 , vital:72670 , https://doi.org/10.1145/1632149.1632170
- Description: Packet analysis is an important aspect of network security, which typi-cally relies on a flexible packet filtering system to extrapolate important packet information from each processed packet. Packet analysis is a computationally intensive, highly parallelisable task, and as such, clas-sification of large packet sets, such as those collected by a network tel-escope, can require significant processing time. We wish to improve upon this, through parallel classification on a GPU. In this paper, we first consider the OpenCL architecture and its applicability to packet analy-sis. We then introduce a number of packet demultiplexing and routing algorithms, and finally present a discussion on how some of these techniques may be leveraged within a GPGPU context to improve packet classification speeds.
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gpf: A GPU accelerated packet classification tool
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2009
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/428103 , vital:72486 , https://d1wqtxts1xzle7.cloudfront.net/67098560/gPF_A_GPU_Accelerated_Packet_Classificat20210505-17707-zqqa4s.pdf?1620201469=andresponse-content-disposi-tion=inline%3B+filename%3DgPF_A_GPU_Accelerated_Packet_Classificat.pdfandExpires=1714733902andSignature=NQ~1DjH1XOuqF8u1Yq74XyG7kp~y0II81vu40SuWO2GQhSgToTHC7ynbAoP3MGv9do~bX1PCAp2Z2TCKUVHT7CmYNRxDmnpk5G4kefH--0VotMHVtFnHnf5Q9nhrp0MIgSxEhncOrlRx5K5sRhlLkyfDib3RS8Y8vu~FIPvm1DaZrfqCZSpXKmHh9r1etybRBRtUokzayPtgbhE41bQtW9wI8J4-JTQ9doyNC-JflFuEfUnhv5Phf45lr7TALm8G8nGZBp3z9-nSLZDxls2mvvVIANCdutyOMDnMDadGoqjIB2wYwUy~Fm424ZWj7fF89Ytj9xqIU63H4NFE2HodtQ__andKey-Pair-Id=APKAJLOHF5GGSLRBV4ZA
- Description: This paper outlines the design of gPF, a fast packet classifier optimised for parallel execution on current generation commodity graphics hard-ware. Specifically, gPF leverages the potential for both the parallel classi-fication of packets at runtime, and the use of evolutionary mechanisms, in the form of a GP-GPU genetic algorithm to produce contextually opti-mised filter permutations in order to reduce redundancy and improve the per-packet throughput rate of the resultant filter program. This paper demonstrates that these optimisations have significant potential for im-proving packet classification speeds, particularly with regard to bulk pack-et processing and saturated network environments.
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Investigating the effect of Genetic Algorithms on Filter Optimisation Within Fast Packet Classifiers
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2009
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/428674 , vital:72528 , https://www.researchgate.net/profile/Marijke-Coet-zee/publication/220803190_A_Framework_for_Web_Services_Security_Policy_Negotiation/links/0fcfd50f7d806aafc8000000/A-Framework-for-Web-Services-Security-Policy-Negotiation.pdf#page=119
- Description: Packet demultiplexing and analysis is a core concern for network secu-rity, and has hence inspired numerous optimisation attempts since their conception in early packet demultiplexing filters such as CSPF and BPF. These optimisations have generally, but not exclusively, focused on improving the speed of packet classification. Despite these im-provements however, packet filters require further optimisation in order to be effectively applied within next generation networks. One identified optimisation is that of reducing the average path length of the global filter by selecting an optimum filter permutation. Since redundant code generation does not change the order of computation, the initial filter order before filter optimisation affects the average path length of the resultant control-flow graph, thus selection of an optimum permutation of filters could provide significant performance improvements. Unfortu-nately, this problem is NP-Complete. In this paper, we consider using Genetic Algorithms to’breed’an optimum filter permutation prior to re-dundant code elimination. Specifically, we aim to evaluate the effec-tiveness of such an optimisation in reducing filter control flow graphs.
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