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A quantum random walk optimization model and algorithm in network cluster server traffic control and task scheduling is proposed. In order to solve the problem of server load balancing, we research and discuss the distribution theory of energy field in quantum mechanics and apply it to data clustering. We introduce the method of random walk and illuminate what the quantum random walk is. Here, we mainly research the standard model of one-dimensional quantum random walk. For the data clustering problem of high dimensional space, we can decompose onem-dimensional quantum random walk intomone-dimensional quantum random walk. In the end of the paper, we compare the quantum random walk optimization method with GA (genetic algorithm), ACO (ant colony optimization), and SAA (simulated annealing algorithm). In the same time, we prove its validity and rationality by the experiment of analog and simulation.

A quantum optimization scheme in network cluster server task scheduling is proposed. We explore and research the distribution theory of energy field in quantum mechanics; specially, we apply it to data clustering. We compare the quantum optimization method with genetic algorithm (GA), ant colony optimization (ACO), simulated annealing algorithm (SAA). At the same time, we prove its validity and rationality by analog simulation and experiment.

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