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.

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.