Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA‐LS, which is a heuristic and self‐adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA‐LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA‐LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long‐term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA‐LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high‐effective and meaningful.

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS‐ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC‐based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC‐based process to faster get the final optimal solution. As a result, the whole approach achieves a longer‐term efficient optimization for power saving. The experimental results demonstrate that PS‐ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high‐effective and meaningful.

Sandstone type uranium is the most valuable and has the most potential for mining among the known uranium deposits. In the process of forming, the hydrolytic migration and enrichment of uranium require special basin sedimentary environment and tectonic background. Therefore, the mineralization process of sandstone type uranium deposits has certain layering characteristics and distribution rules in the underground vertical depth space. It is important to mine the spatial distribution characteristics of vertical uranium-bearing layers, and thus, reconstruct the three-dimensions of uranium orebodies. In this paper, according to the metallogenic law and distribution characteristics of sandstone type uranium in the underground vertical space, a nonlinear uranium-bearing layers identification (NULI) method of sandstone type uranium is proposed by using different types, resolutions and scales of borehole data. Then, the depth of uranium mineralization for the Daying uranium deposit within northern Ordos Basin is identified accurately and the spatial distribution characteristics of the uranium-bearing layer on the exploration line are obtained. Finally, the occurrence mode of the underground uranium orebodies are presented by using three-dimensional reconstruction analysis. It provides a basis for the prediction, exploration and mining of sandstone type uranium deposits within the Ordos Basin.