A high-level architecture for efficient packet trace analysis on gpu co-processors
- Nottingham, Alastair, Irwin, Barry V W
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429572 , vital:72623 , 10.1109/ISSA.2013.6641052
- Description: This paper proposes a high-level architecture to support efficient, massively parallel packet classification, filtering and analysis using commodity Graphics Processing Unit (GPU) hardware. The proposed architecture aims to provide a flexible and efficient parallel packet processing and analysis framework, supporting complex programmable filtering, data mining operations, statistical analysis functions and traffic visualisation, with minimal CPU overhead. In particular, this framework aims to provide a robust set of high-speed analysis functionality, in order to dramatically reduce the time required to process and analyse extremely large network traces. This architecture derives from initial research, which has shown GPU co-processors to be effective in accelerating packet classification to up to tera-bit speeds with minimal CPU overhead, far exceeding the bandwidth capacity between standard long term storage and the GPU device. This paper provides a high-level overview of the proposed architecture and its primary components, motivated by the results of prior research in the field.
- Full Text:
- Date Issued: 2013
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429572 , vital:72623 , 10.1109/ISSA.2013.6641052
- Description: This paper proposes a high-level architecture to support efficient, massively parallel packet classification, filtering and analysis using commodity Graphics Processing Unit (GPU) hardware. The proposed architecture aims to provide a flexible and efficient parallel packet processing and analysis framework, supporting complex programmable filtering, data mining operations, statistical analysis functions and traffic visualisation, with minimal CPU overhead. In particular, this framework aims to provide a robust set of high-speed analysis functionality, in order to dramatically reduce the time required to process and analyse extremely large network traces. This architecture derives from initial research, which has shown GPU co-processors to be effective in accelerating packet classification to up to tera-bit speeds with minimal CPU overhead, far exceeding the bandwidth capacity between standard long term storage and the GPU device. This paper provides a high-level overview of the proposed architecture and its primary components, motivated by the results of prior research in the field.
- Full Text:
- Date Issued: 2013
Towards a GPU accelerated virtual machine for massively parallel packet classification and filtering
- Nottingham, Alastair, Irwin, Barry V W
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/430295 , vital:72681 , https://doi.org/10.1145/2513456.2513504
- Description: This paper considers the application of GPU co-processors to accelerate the analysis of packet data, particularly within extremely large packet traces spanning months or years of traffic. Discussion focuses on the construction, performance and limitations of the experimental GPF (GPU Packet Filter), which employs a prototype massively-parallel protocol-independent multi-match algorithm to rapidly compare packets against multiple arbitrary filters. The paper concludes with a consideration of mechanisms to expand the flexibility and power of the GPF algorithm to construct a fully programmable GPU packet classification virtual machine, which can perform massively parallel classification, data-mining and data-transformation to explore and analyse packet traces. This virtual machine is a component of a larger framework of capture analysis tools which together provide capture indexing, manipulation, filtering and visualisation functions.
- Full Text:
- Date Issued: 2013
Towards a GPU accelerated virtual machine for massively parallel packet classification and filtering
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/430295 , vital:72681 , https://doi.org/10.1145/2513456.2513504
- Description: This paper considers the application of GPU co-processors to accelerate the analysis of packet data, particularly within extremely large packet traces spanning months or years of traffic. Discussion focuses on the construction, performance and limitations of the experimental GPF (GPU Packet Filter), which employs a prototype massively-parallel protocol-independent multi-match algorithm to rapidly compare packets against multiple arbitrary filters. The paper concludes with a consideration of mechanisms to expand the flexibility and power of the GPF algorithm to construct a fully programmable GPU packet classification virtual machine, which can perform massively parallel classification, data-mining and data-transformation to explore and analyse packet traces. This virtual machine is a component of a larger framework of capture analysis tools which together provide capture indexing, manipulation, filtering and visualisation functions.
- Full Text:
- Date Issued: 2013
Capturefoundry: a gpu accelerated packet capture analysis tool
- Nottingham, Alastair, Richter, John, Irwin, Barry V W
- Authors: Nottingham, Alastair , Richter, John , Irwin, Barry V W
- Date: 2012
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/430112 , vital:72666 , https://doi.org/10.1145/2389836.2389877
- Description: Packet captures are used to support a variety of tasks, including network administration, fault diagnosis and security and network related research. Despite their usefulness, processing packet capture files is a slow and tedious process that impedes the analysis of large, long-term captures. This paper discusses the primary components and observed performance of CaptureFoundry, a stand-alone capture analysis support tool designed to quickly map, filter and extract packets from large capture files using a combination of indexing techniques and GPU accelerated packet classification. All results are persistent, and may be used to rapidly extract small pre-filtered captures on demand that may be analysed quickly in existing capture analysis applications. Performance results show that CaptureFoundry is capable of generating multiple indexes and classification results for large captures at hundreds of megabytes per second, with minimal CPU and memory overhead and only minor additional storage space requirements.
- Full Text:
- Date Issued: 2012
- Authors: Nottingham, Alastair , Richter, John , Irwin, Barry V W
- Date: 2012
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/430112 , vital:72666 , https://doi.org/10.1145/2389836.2389877
- Description: Packet captures are used to support a variety of tasks, including network administration, fault diagnosis and security and network related research. Despite their usefulness, processing packet capture files is a slow and tedious process that impedes the analysis of large, long-term captures. This paper discusses the primary components and observed performance of CaptureFoundry, a stand-alone capture analysis support tool designed to quickly map, filter and extract packets from large capture files using a combination of indexing techniques and GPU accelerated packet classification. All results are persistent, and may be used to rapidly extract small pre-filtered captures on demand that may be analysed quickly in existing capture analysis applications. Performance results show that CaptureFoundry is capable of generating multiple indexes and classification results for large captures at hundreds of megabytes per second, with minimal CPU and memory overhead and only minor additional storage space requirements.
- Full Text:
- Date Issued: 2012
Extending the NFComms: framework for bulk data transfers
- Nottingham, Alastair, Irwin, Barry V W
- 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.
- Full Text:
- Date Issued: 2009
- 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.
- Full Text:
- Date Issued: 2009
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