Concerning the problem of data sparseness, cold start and low user experience of recommendation system, a video recommendation algorithm based on clustering and hierarchical model was proposed to improve the performance of recommendation system and user experience. Focusing on the user, similar users were obtained by analyzing Affiliation Propagation (AP) cluster, then historical data of online video of similar users was collected and a recommendation set of videos was geberated. Secondly, the user preference degree of a video was calculated and mapped into the tag weight of the video. Finally, a recommendation list of videos was generated by using analytic hierarchy model to calculate the ranking of user preference with videos. The experimental results on MovieLens Latest Dataset and YouTube video review text dataset show that the proposed algorithm has good performance in terms of Root-Mean-Square Error (RMSE) and the recommendation accuracy.

Enhancing the accuracy of link prediction is one of the fundamental problems in the research of complex networks. The existing node similarity-based prediction indexes do not make full use of the importance influences of the nodes in the network. In order to solve the above problem, a link prediction algorithm based on the node importance was proposed. The node degree centrality, closeness centrality and betweenness centrality were used on the basis of similarity indexes such as Common Neighbor (CN), Adamic-Adar (AA) and Resource Allocation (RA) of local similarity-based link prediction algorithm. The link prediction indexes of CN, AA and RA with considering the importance of nodes were proposed to calculate the node similarity. The simulation experiments were taken on four real-world networks and Area Under the receiver operation characteristic Curve (AUC) was adopted as the standard index of link prediction accuracy. The experimental results show that the link prediction accuracies of the proposed algorithm on four data sets are higher than those of the other comparison algorithms, like Common Neighbor (CN) and so on. The proposed algorithm outperforms traditional link prediction algorithm and produces more accurate prediction on the complex network.

Aiming at the fact that the traditional collaborative filtering algorithm cannot deeply mine user relationship and recommend new items to users, a Trust and Item Preference Collaborative Filtering (TIPCF) recommendation algorithm was proposed. Firstly, in order to mine the latent trust relationship of the users, the user reliability was gotten and the trust degree between users was quantified by analyzing user ratings. Secondly, by considering that the difference of users' preference for different target items has an effect on user similarity, user preference was added to the traditional user similarity algorithm to improve the similarity algorithm. Thirdly, the choice of nearest neighbor set was more accurate by incorporating user reliability and improved similarity. Finally, the users' preference on item attribute was used to recommend new items. Experimental results show that, compared with traditional collaborative algorithm, the Mean Absolute Error (MAE) of TIPCF was decreased by 6.7%, and the MAE of TIPCF was decreased by 10.7% when recommending new items on the Movielens dataset. TIPCF not only improves the accuracy of recommendation, but also increases the recommended probablity of new items.

Like MapReduce, tasks under big data environment are always with data-dependent constraints. The resource selection strategy in distributed storage system trends to choose the nearest data block to requestor, which ignored the server's resource load state, like CPU, disk I/O and network, etc. On the basis of the distributed storage system's cluster structure, data file division mechanism and data block storage mechanism, this paper defined the cluster-node matrix, CPU load matrix, disk I/O load matrix, network load matrix, file-division-block matrix, data block storage matrix and data block storage matrix of node status. These matrixes modeled the relationship between task and its data constraints. And the article proposed an optimal resource selection algorithm with data-dependent constraints (ORS2DC), in which the task scheduling node is responsible for base data maintenance, MapRedcue tasks and data block read tasks take different selection strategies with different resource-constraints. The experimental results show that, the proposed algorithm can choose higher quality resources for the task, improve the task completion quality while reducing the NameNode's load burden, which can reduce the probability of the single point of failure.

Through the analysis and research of reliability problems in the existing workflow scheduling algorithm, the paper proposed a reliability-based workflow strategy concerning the problems in improving the reliability of the entire workflow by sacrificing efficiency or money in some algorithms. Combining the reliability of tasks in workflow and duplication ideology, and taking full consideration of priorities among tasks, this strategy lessened failure rate in transmitting procedure and meantime shortened transmit time, so it not only enhanced overall reliability but also reduced makespan. Through the experiment and analysis, the reliability of cloud workflow in this strategy, tested by different numbers of tasks and different Communication to Computation Ratios (CCR), was proved to be better than the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and its improved algorithm named SHEFTEX, including the superiority of the proposed algorithm over the HEFT in the completion time.

Concerning the poor quality of recommendations of traditional user-based and item-based collaborative filtering models, a new collaborative filtering model combining users and items predictions was proposed. Firstly, it considered both users and items, and optimized the similarity model with excellent performance dynamically. Secondly, it constructed neighbor sets for the target objects by selecting some similar users and items according to the similarity values, and then obtained the user-based and item-based prediction results respectively based on some prediction functions. Finally, it gained final predictions by using the adaptive balance factor to coordinate both of the prediction results. Comparative experiments were carried out under different evaluation criteria, and the results show that, compared with some typical collaborative filtering models such as RSCF, HCFR and UNCF, the proposed model not only has better performance in prediction accuracy of items, but also does well in the precision and recall of recommendations.

An adaptive Virtual Machine (VM) dynamic migration strategy of soft energy-saving was put forward to optimize energy consumption and load balance in cloud computing. The energy-saving strategy adopted Dynamic Voltage Frequency Scaling (DVFS) as the static energy-aware technology to achieve the sub-optimized static energy saving, and used online VM migration to achieve an adaptive dynamic soft energy-saving in cloud platform. The two energy-saving strategies were simulated and compared with each other in CloudSim platform, and the data were tested on PlanetLab platform. The results show that: Firstly, the adaptive soft and hard combination strategy in energy-saving can significantly save 96% energy; secondly, DVFS+MAD_MMT strategy using Median Absolute Deviation (MAD) to determine whether the host is overload, and choosing VM to remove based on Minimum Migration Time (MMT), which can save energy about 87.15% with low-load in PlanetLab Cloudlets than that of experimental environment; finally, security threshold of 2.5 in MAD_MMT algorithm can consume the energy efficiently and achieve the adaptive load balancing of virtual machines migration dynamically.

The traditional User-based Collaborative Filtering (UCF) models do not consider the attributes of items fully in the process of measuring the similarity of users. In view of the drawback, this paper proposed two collaborative filtering recommendation models considering item attributes. Firstly, the models optimized the rating-based similarity between users, and then summed the rating numbers of different items by users according to item attributes, in order to obtain the optimized and attribute-based similarity between users. Finally, the models coordinated the two types of similarity measurements by a self-adaptive balance factor, to complete the item prediction and recommendation process. The experimental results demonstrate that the newly proposed models not only have reasonable time costs in different data sets, but also yield excellent improvements in prediction accuracy of ratings, involving an average improvement of 5%, which confirms that the models are efficient in improving the accuracy of user similarity measurements.

According to the research and analysis on the existing task scheduling strategy of scientific workflow under the cloud environment, a two-step task scheduling strategy was proposed. This strategy aimed at solving or alleviating the phenomenon of resource idle in Heterogeneous Earliest Finish Time (HEFT) algorithm and SHEFT algorithm. Along with the characteristics of cloud computing environment, it derives from the SHEFT algorithm. It can make the most use of the resources idle time and get the minimum makespan. The experiments and performance analysis for the scheduling strategy show that it has a significant improvement in the workflow makespan and resource utilization.