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Evolutionary design and optimization of components used in high-speed computer networks
Grochol, David ; Sekaj, Ivan (oponent) ; Jašek, Roman (oponent) ; Sekanina, Lukáš (vedoucí práce)
The research presented in this thesis is directed toward the evolutionary optimization of selected components of network applications intended for high-speed network monitoring systems. The research started with a study of current network monitoring systems. As an experimental platform, the Software Defined Monitoring (SDM) system was chosen. Because traffic processing is an important part of all monitoring systems, it was analyzed in greater detail. For detailed studies conducted in this thesis, two components were selected: the classifier of application protocols and the hash functions for network flow processing. The evolutionary computing techniques were surveyed with the aim to optimize not only the quality of processing but also the execution time of evolved components. The single-objective and multi-objective versions of evolutionary algorithms were considered and compared. A new approach to the application protocol classifier design was proposed. Accurate and relaxed versions of the classifier were optimized by means of Cartesian Genetic Programming (CGP). A significant reduction in Field-Programmable Gate Array (FPGA) resources and latency was reported.Specialized, highly optimized network hash functions were evolved by parallel Linear Genetic Programming (LGP). These hash functions provide better functionality (in terms of quality of hashing and execution time) than the state-of-the-art hash functions. Using multi-objective LGP, we even improved the hash functions evolved with the single-objective LGP. Parallel pipelined hash functions were implemented in an FPGA and evaluated for purposes of network flow hashing. A new reconfigurable hash function was developed as a combination of selected evolved hash functions. Very competitive general-purpose hash functions were also evolved by means of multi-objective LGP and evaluated using representative data sets. The multi-objective approach produced slightly better solutions than the single-objective approach. We confirmed that common LGP and CGP implementations can be used for automated design and optimization of selected components; however, it is important to properly handle the multi-objective nature of the problem and accelerate time-critical operations of GP.
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