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The current storage system is facing the bottleneck of performance due to the gap between fast CPU computing speed and the slow response time of hard disk. Recently a multitier hybrid storage system (MTHS) which uses fast flash devices like a solid-state drive (SSD) as the one of the high performance storage tiers has been proposed to boost the storage system performance. In order to maintain the overall performance of the MTHS, optimal disk storage assignment has to be designed so that the data migrated to the high performance tier like SSD is the optimal set of data. In this paper we proposed a optimal data allocation algorithm for disk storage in MTHS. The data allocation problem (DAP) is to find the optimal lists of data files for each storage tier in the MTHS to achieve maximal benefit values without exceeding the available size of each tier. We formulate the DAP as a special multiple choice knapsack problem (MCKP) and propose the multiple-stage dynamic programming (MDP) to find the optimal solutions. The results show that the MDP can achieve improvements up to 6 times compared with the existing greedy algorithms.

We present a method to compute the delay constrained multicast routing tree by employing chaotic neural networks. Experimental result shows that the noisy chaotic neural network (NCNN) provides optimal solution more often compared to the transiently chaotic neural network (TCNN) and the Hopfield neural network (HNN). Furthermore, compared with the bounded shortest multicast algorithm (BSMA), the noisy chaotic neural network is able to find multicast trees with lower cost.

We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by rearranging frequency assignments. The NCNN-VT model consists N times M of noisy chaotic neurons for an N-carrier M-segment problem. The NCNN-VT facilitates the interference minimization by mapping the objective to variable thresholds (biases) of the neurons. The performance of the NCNN-VT is demonstrated by solving a set of benchmark problems and randomly generated test instances. The NCNN-VT achieves better solutions, i.e., smaller interference with much lower computation cost compared to existing algorithms.

A new model of cellular neural networks (CNNs) with transient chaos is proposed by adding negative self-feedbacks into CNNs after transforming the dynamic equation to discrete time via Euler's method. The simulation on the single neuron model shows stable fix points, bifurcation and chaos. Hence, this new CNN model has richer and more flexible dynamics, and therefore may possess better capabilities of solving various problems, compared to the conventional CNN with only stable dynamics