2019 IEEE 27th International Conference on Network Protocols (ICNP) 2019
DOI: 10.1109/icnp.2019.8888129
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FlowShader: a Generalized Framework for GPU-accelerated VNF Flow Processing

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Cited by 9 publications
(3 citation statements)
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“…Research on VPN gateways can be classified into three categories: solutions to network interconnections [15][16][17], assurance of security issues [18,19] and evaluations and improvements of network performance. Evaluations and improvements of network performance range from evaluations of VPN gateways [20][21][22][23][24] and hardware-accelerated VPN gateways [11,12,25,26] to improvements of software VPN gateways [13,[27][28][29].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Research on VPN gateways can be classified into three categories: solutions to network interconnections [15][16][17], assurance of security issues [18,19] and evaluations and improvements of network performance. Evaluations and improvements of network performance range from evaluations of VPN gateways [20][21][22][23][24] and hardware-accelerated VPN gateways [11,12,25,26] to improvements of software VPN gateways [13,[27][28][29].…”
Section: Related Workmentioning
confidence: 99%
“…In terms of hardware-accelerated VPN gateways, Yi et al [25] propose a GPU acceleration framework to achieve both high generality and throughput under skewed flow size distributions. Raumer et al [26] implement a VPN security gateway application, based on MoonGen and IPsec ESP tunnel mode, to demonstrate and evaluate the VPN throughput and energy saving performance of the improved device driver.…”
Section: Related Workmentioning
confidence: 99%
“…The platform exposes a programming abstraction to give applications direct control over F-NIC accelerators and a I/O path virtualization to let multiple VMs share the F-NIC with guaranteed security and fairness. FlowShader [129], GPUNFV [130], Gen [131], Grus [132], and G-NET [133] seek to construct a concerted CPU-GPU pipeline to expedite SFC processing. FlowShader explores GPU and CPU for paralleled processing.…”
Section: Single Nfvi-pop Optimizationmentioning
confidence: 99%