Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide. However, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse gas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also to minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs) with deterministic and stochastic demands. In order to ensure a quick response to VM requests and improve the energy efficiency, a two-phase optimization strategy has been proposed, in which VMs are deployed in runtime and consolidated into servers periodically. Based on an improved multidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE) and a live migration algorithm based on the basic set (LMABBS) are, respectively, developed for each phase. Experimental results have shown that under different VMs’ stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a defined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also, the difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational efficiency.
A circumferential shear horizontal (CSH) guide wave-detection method using a periodic permanent magnet electromagnetic acoustic transducer (PPM EMAT) was proposed to solve the defect detection located at the inside of the pipe welded by supporting structures. Firstly, a low-frequency CSH0 mode was selected to establish a three-dimensional equivalent model for the defect detection to cross the pipe support, and the ability of the CSH0 guided wave to propagate through the support and weld structure was analyzed. Then, an experiment was used for the further exploration of the influence of different sizes and types of defects on detection after using the support, as well as the ability of detection mechanism to cross different pipe structures. The results show that both the experiment and the simulation received a good detection signal at 3 mm crack defects, which proves that the method can detect the defects by crossing the welded supporting structure. At the same time, the support structure shows a greater impact on the detection of small defects than the welded structure. The research in this paper can provide ideas for guide wave detection across the support structure in the future.
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