On the basis of the classical multiple knapsack problem， the Heterogeneous Multiple Knapsack Problem （HMKP） was proposed， which was abstracted from the commonalities of typical logistics service scenarios. And， an Imperialist Competitive Algorithm （ICA） was designed and customized to solve HMKP. As the origin ICA is easy to fall into the local optimum and the optimal solution of the 0-1 knapsack problem is usually near the constraint boundary， Two-Point Automutation Strategy （TPAS） and Jump out of Local Optimum Algorithm （JLOA） were designed to improve ICA， and a Binary Imperialist Competitive Algorithm （BICA） for 0-1 knapsack problem was presented. BICA showed comprehensive and efficient optimization ability in solving 35 numerical examples of 0-1 knapsack problem. BICA based on Best-Matched Value （BMV） was able to find the ideal optimal solutions of 19 out of 20 examples with 100% success rate in the first test set， and the ideal optimal solutions of 12 out of 15 examples were found by the above algorithm with 100% success rate in the second test set， achieving the best performance of all the comparison algorithms. The numerical analysis results show that BICA maintains the multipolar development strategy in the optimization evolution and relies on the unique population evolution method to search the ideal solution in the solution space efficiently. Subsequently， aiming at the strong constraint and high complexity of HMKP， a Multiple Level Binary Imperialist Competitive Algorithm （MLB-ICA） for solving HMKP was put forward based on BICA. Finally， the numerical experiments and performance evaluation of MLB-ICA were carried out on a high dimensional HMKP test set constructed by combining multiple typical numerical examples of 0-1 knapsack problems. The results showed that the solving time of MLB-ICA is longer than that of Gurobi solver， but the solving accuracy of MLB-ICA is 28% higher than that of Gurobi solver. It can be seen that MLB-ICA can solve high-dimensional complicated HMKP efficiently with low computational cost within acceptable time， and provides a feasible algorithm design scheme for ICA to solve super-large scale combinatorial optimization problems.

RocksDB key-value storage system based on Log-Structured Merge （LSM） tree has the problem of low read performance caused by the constraints of its hierarchical structure. One effective solution is to cache hot spot data proactively， but it faces two challenges. One is how to predict the hot spot data when the data distribution keeps on changing constantly， the other is how to integrate the proactive caching mechanism with the RocksDB storage structure. To tackle these challenges， a proactive caching framework for RocksDB key-value system with multiple components including data collection， system interaction and system evaluation was built， which can cache the hot spot data at the low levels of the LSM tree. And with the modeling of data access patterns， an incremental learning based prediction analysis method for hot spot data was designed and implemented， which can reduce the number of I/O operations of storage medium. Experimental results show that the proposed mechanism can effectively improve the read performance of RocksDB under different dynamic workloads.

Distributed matrix multiplication is a fundamental operation in many distributed machine learning and scientific computing applications， but its performance is greatly influenced by the stragglers commonly existed in the systems. Recently， researchers have proposed a fountain code based coded matrix multiplication method， which can effectively mitigate the effect of stragglers by fully exploiting the partial results of stragglers. However， it lacks the consideration of the storage cost of worker nodes. By considering the tradeoff relationship between the storage cost and the finish time of computation， the computational deadline-aware storage optimization problem for heterogeneous worker nodes was proposed firstly. Then， through the theoretical analysis， the solution based on expectation approximation was presented， and the problem was transformed into a convex optimization problem by relaxation for efficient solution. Simulation results show that in the case of ensuring a large task success rate， the storage overhead of the proposed scheme will rapidly decrease as the task duration is relaxed， and the scheme can greatly reduce the storage overhead brought by encoding. In other words， the proposed scheme can significantly reduce the extra storage overhead while guaranteeing that the whole computation can be finished before the deadline with high probability.

Most of the variants of Graph Cut algorithm do not impose any shape constraints on the segmentations, rendering it difficult to obtain semantic valid segmentation results. As for pedestrian segmentation, this difficulty leads to the non-human shape of the segmented object. An improved Graph Cut algorithm combining shape priors and discriminatively learned appearance model was proposed in this paper to segment pedestrians in static images. In this approach, a large number of real pedestrian silhouettes were used to encode the a'priori shape of pedestrians, and a hierarchical model of pedestrian template was built to reduce the matching time, which would hopefully bias the segmentation results to be humanlike. A discriminative appearance model of the pedestrian was also proposed in this paper to better distinguish persons from the background. The experimental results verify the improved performance of this approach.