Automated task allocation for network processors using genetic algorithm

Network Processors (NPs) are embedded system-on-a-chip multiprocessors that are optimized to perform simple packet processing tasks at data rates of several Gigabytes per second. They are the key components to build a performance-scalable and function-flexible network processing system. To meet the performance demands of increasing link speeds and more complex network applications, NPs are implemented with several dozen processor cores and run multiple packet processing applications in parallel. This trend makes it increasingly difficult for application developers to program NPs for high performance. This thesis presents an automated task scheduling technique to address this programming complexity.; Our proposed technique is based on genetic algorithm. By incorporating the tasks dependency aware mapping and encoding task scheduling list as a chromosome, it can quickly remove the invalid solutions and evolve to the high quality solutions. This technique takes advantage of task-level and packet-level parallelism to maximize system performance for any given NPs architecture. The simulation results show that the proposed technique can generate high quality mapping comparing to other heuristics by mapping some sample network applications. The results of this work will enable researchers and engineers to systematically evaluate and quantitatively understand the NPs system issues including application partitioning, architecture organizing, workload mapping and run-time operating.