Research On Key Technologies Of Hierarchical Storage System Management And Protection Based On Evolution Algorithms

With the vigorous development of the social productive forces, information technology is applied deeply into people’s daily lives, accompanied by the high requirements of massive data processing. The most important computer system, Hierarchical Storage system also faces some huge challenges:information explosion with noise data, grim requirements for useful information distribution and extract; timeliness of personalized data access and the security demands of the data storage. Existing data processing technology in storage system have shown their advantages. At the same time there are also some inherent shortages while processing the large-scale data. The reason is that while facing a large-scale data, some of the traditional treatment method arise the complexity exponentially with the amount of data increases in terms of the complexity of the space and the time. Evolution algorithms have shown their efficiency while dealing with the large-scale data. It is reasonable and worthy that the tools of genetic algorithm is introduced to solve the above problems of the corresponding hierarchical storage platform.Aiming at the problem of prefetching algorithm, we developed a novel evolutionary algorithm, namely Uniform-Design Gene Expression Programming (UGEP).UGEP uses (1) a mixed-level uniform table for generating initial population and (2) multi-parent crossover operators adaptive to the dispersibility of uniform-design. In addition to theoretic analysis, we compared UGEP to existing GEP variants via a number of experiments in dealing with four real data sets of the problems of prefetching algorithm. Both theoretic analysis and experimental results indicate that UGEP excels in terms of both achieving the global optimum and the convergence speed. In experiments of four real workflow tests, UGEP have achieved good results in terms of the hit rate and response time.In order to improve the performance of association rules mining algorithm when disposing big data set, a novel GEP based on Niche (NGEP) is presented to solve the problem. The procedure of NGEP begins with the niche evolution and then fuses some of the sub-niches according to the similarity of best individuals. And then Cartesian generated nested in the kernel set of niches to generate better outcomes. The experimental results show that our algorithm performs better than the other similar evolutionary algorithm in terms of diversity of population and precision; besides, it can discover more association rules. In the evaluation tests of the real traces, NGEP applied in data layout has showed its advantages while in sequence prefetching.Storage-based intrusion detection systems (SIDS) allow storage systems to watch for suspicious activity. We present a novel storage-based intrusion detection scheme to monitor the user’s activities using the artificial immune technique. Compared with the previous SIDS prototype, the SIDS using artificial immune technique can continue to operate even meets a strange suspicious behavior. a set of appropriate parameters of algorithm are fitted according to the mean convergence speed and detection efficiency. The simulation shows the proposed scheme can reach higher detection rate and lower false alarm rate than the previous.Through the research on the above aspects, the variants of evolution computing are applied into hierarchical storage platform management mechanism. Thus, the data storage management and protection methods for the hierarchical storage platform can be built successfully.