Concerning the problem of high energy consumption existing in Cloud storage Content Distribution Network (CCDN), the energy consumption optimization method for the CCDN was studied. Firstly, the operation principle of CCDN was analysed. Then, the energy consumption formulas were given for each cloud server and each network link. Moreover, the weighted graph was used to describe the whole network. Furthermore, based on the weighted graph, an energy consumption optimization algorithm named Min-Energy-Graph (MEG) was designed to satisfy the Quality of Service (QoS) of CCDN and data distribution of network system. MEG was compared with Greedy Site (GS) and Optimal Static Placement and Routing (OSPR) by the simulation experiments. Compared with the GS and OSPR, the energy consumption of MEG was reduced by 6.6% and 30% respectively in the experiment of system extension, the energy consumption of MEG was reduced by 28.9% and 60.2% separately in the experiment of ensuring the user QoS, and the energy consumption of MEG was reduced by 32.2% and 89.3% independently in the experiment of network topology density. The experimental results show that the proposed energy management method can greatly reduce the energy consumption of CCDN.

Concerning the problem that previous studies mostly consider from the resource provider's perspective, and user's evaluations have not been fully utilized to improve the resource decision making ability, this paper proposed a resource re-allocation method focusing on the user's evaluation feedback. First, through analyzing the process of cloud resource allocation, several factors influencing decision-making were defined, and an adaptive cloud resource management framework with user's involvement was proposed. Next, the main idea of method of resource re-allocation with user's involvement was elaborated, and a formula was designed to guide user's evaluation. Finally, based on similarity theory, the user's expected satisfaction of a new cloud task was predicted. Together with the cloud task parameters and environment parameters, it was used to be the input of BP (Back Propagation) neural network to make the resource allocation decision. In the comparison experiments with the allocation scheme without user's involvement, the average user's satisfactory of the proposed scheme increased by 7.4%, maintained at more than 0.8, showed a steady upward trend. In the comparison experiments with Min-Max algorithm and Cloud Tasks-Resources Satisfactory Matching (CTRSM) algorithm, its average user's satisfactory increased by 16.7% and 4.6% respectively. The theoretical analysis and simulation results show that the cloud resource re-allocation method focusing on user's evaluation is self-improved, and it can improve the adaptive ability of cloud resource management.

In E-commerce website, massive disorder shopping reviews may make the consumers be lost in the massive shopping reviews and can not distinguish trusted reviews. Therefore, this paper proposed a trustworthy sort method for customer reviews. Firstly, focusing on commercial advertising information in websites and concerning about whether the contents of the online customer reviews and product functional properties are closely related, the authors designed an algorithm of product's key features extractions from shopping websites based on HTML script format, and presented a method of customer reviews features extractions based on natural language processing. Secondly, the authors used the technique of words similarity to analyze the correlation degree between product features and customer reviews contents, and then proposed the computational method of trust degree for shopping customer reviews. Finally, through analyzing the method with an example, the proposed method achieves a trustworthy sort for large online shopping customer reviews. Thus customers need not browse all reviews to judge which one can be trusted or have the real reference value. It decreases information search costs and improves the efficiency of decision making.

Concerning the problem that previous studies on the scalability do not fully consider parallel execution time, and the relationships between latency scalability and parallel execution time have not been yet studied thoroughly, this paper studied the relationships between latency scalability and parallel execution time deeply and fully. Thereby some important conclusions were drawn, and they were about the relationships between latency scalability and parallel execution time after different algorithm-machines were extended from the same initial state. Then the proof of the above conclusions was given in this paper. The derived conclusions enriched the research content about the relationships between latency scalability and parallel execution time and provided a theoretical basis for obtaining ideal latency scalability of parallel computing. Finally the important conclusions and analytical expressions were verified through experimental results obtained for different algorithm-machines.

Extending in parallel computing is an effective approach to achieve higher computing performance. However, under the constraint of the fixed structure, it is difficult to improve the performance of parallel computing only by extending its scale simply. Concerning such extension problem of parallel computing, this paper investigated the factors from architecture and parallel tasks which affect its scalability, and modeled the architecture as well as parallel tasks by the weighted graph. Then, an extension method was proposed that the critical path remained unchanged. The novel extension method, in essence, did not change the graph’s structure and only adjusts the graph’s weights. Additionally, some conclusions about the new extension method were drawn by further derivation. Finally, the simulative experiments on the platform SimGrid were conducted to test the effectiveness of the proposed extension method. The results show that the proposed method can solve such extension problem, and at the same time it also realize isospeed-e extension. it can help guide practical extensions of parallel computing.

Aiming at the problem that the performance of parallel computing cannot be improved by extending its scale under the constraint of fixed structure, a method of proportionally adjusting graph weights was proposed to handle such extension problem. The method firstly investigated the factors from architecture and parallel tasks which affected its scalability, and then modeled the architecture as well as parallel tasks by using weighted graph. Finally, it realized an extension in parallel computing by adjusting proportionally the weights of the vertices and edges in the graph model for parallel computing. The experimental results show that the proposed extension method can realize isospeed-efficiency extension for parallel computing under the constraint of fixed structure.