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Packet parsing is the basic operation performed at all points of the network infrastructure. Modern networks impose challenging requirements on the performance and configurability of packet parsing modules, however the high-speed parsers often use very large chip area. We propose novel architecture of pipelined packet parser, which in addition to high throughput (over 100 Gb/s) offers also low latency. Moreover, the latency to throughput ratio can be finely tuned to fit the particular application. The parser is handoptimized thanks to the direct implementation in VHDL, yet the structure is very uniform and easily extensible for new protocols.

Packet parsing is among basic operations that are performed at all points of a network infrastructure. Modern networks impose challenging requirements on the performance and configurability of packet parsing modules. However, high-speed parsers often use a significant amount of hardware resources. We propose a novel architecture of a pipelined packet parser for FPGA, which offers low latency in addition to high throughput (over 100 Gb/s). Moreover, the latency, throughput and chip area can be finely tuned to fit the needs of a particular application. The parser is hand-optimized thanks to a direct implementation in VHDL, yet the structure is uniform and easily extensible for new protocols.

Current hardware acceleration cores for network traffic processing are often well optimized for one particular task and therefore provide high level of hardware acceleration. But for many applications, such as network traffic monitoring and security, it is also necessary to achieve rapid development cycle to provide fast response to security threats. We propose and evaluate a new concept of hardware acceleration for flexible flow-based network traffic monitoring with support of application protocol analysis. The concept is called Software Defined Monitoring (SDM) and it relies on a configurable hardware accelerator implemented in FPGA, coupled with smart monitoring tasks running as software on general CPU. The monitoring tasks in the software control the level of detail and type of information retained during the hardware processing. This arrangement allows rapid application prototyping in the software, followed by further shifting of the timing critical parts of the processing to the hardware accelerator. The concept is proposed with the scalability in mind, therefore it is suitable for different FPGA based platforms ranging from embedded single-chip solutions (such as Zynq or Cyclone V) to high-speed backbone network monitoring boxes. Our pilot high-speed implementation using FPGA acceleration board in a commodity server performs a 100 Gb/s flow traffic measurement augmented by a selected application protocol analysis.

Current high-speed network monitoring systems focus more and more on the data from the application layers. Flow data is usually enriched by the information from HTTP, DNS and other protocols. The increasing speed of the network links, together with the time consuming application protocol parsing, require a new way of hardware acceleration. Therefore we propose a new concept of hardware acceleration for flexible flowbased application level monitoring which we call Software Defined Monitoring (SDM). The concept relies on smart monitoring tasks implemented in the software in conjunction with a configurable hardware accelerator. The hardware accelerator is an applicationspecific processor tailored to stateful flow processing. The monitoring tasks reside in the software and can easily control the level of detail retained by the hardware for each flow. This way the measurement of bulk/uninteresting traffic is offloaded to the hardware while the advanced monitoring over the interesting traffic is performed in the software. The proposed concept allows one to create flexible monitoring systems capable of deep packet inspection at high throughput. Our pilot implementation in FPGA is able to perform a 100 Gb/s flow traffic measurement augmented by a selected application-level protocol parsing.

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