In the distributed storage system, Disk space Utilization (DU) is generally used to measure the load balance of each storage node. When given the equal disk space utilization to each node, the balance of storage load is achieved in the whole distributed storage system. However, in practice, the storage node with relatively low disk I/O speed and reliability becomes a bottleneck for the performance of data I/O in the whole storage system. Therefore in heterogeneous distributed storage system and specially the system which has great differences in disk I/O speed and reliability of each storage node, the speed of data I/O is definitely limited when disk space utilization is the only evaluation criteria of storage load balance. A new idea based on read-write efficiency was proposed to measure the storage load balance in the distributed storage system. According to the definition of Storage Entropy (SE) given by the theory of load balance and entropy, a kind of load balance algorithm based on SE was proposed. With system load and single node load determination as well as load shifting, the quantitative adjustment for storage load of the distributed storage system was achieved. The proposed algorithm was tested and compared with the load balance algorithm based on disk space utilization. Experimental results show that the proposed algorithm can balance storage load well in the distributed storage system, which effectively restrains the system load imbalance and improves the overall efficiency of reading and writing of the distributed storage system.

Concerning the problem that traditional recommendation algorithm ignores the time factors, according to the similarity of individuals' short-term behavior, a calculation method of item correlation by using time decay function based on users' interest was proposed. And based on this method, a new item similarity was proposed. At the same time, the TItemRank algorithm was proposed which is an improved ItemRank algorithm by combining with the user interest-based item correlation. The experimental results show that: the improved algorithms have better recommendation effects than classical ones when the recommendation list is small. Especially, when the recommendation list has 20 items, the precision of user interest-based item similarity is 21.9% higher than Cosin similarity and 6.7% higher than Jaccard similarity. Meanwhile, when the recommendation list has 5 items, the precision of TItemRank is 2.9% higher than ItemRank.