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Recently researchers use skyline techniques to optimize service selection procedure, where they can filter those low-quality web services from the large amount of candidates and return a much smaller high-quality service set. The skycube concept is adopted for quickly responding to the skyline queries with different combinations of Quality of Web Service (QoWS) parameters. As the skycube computation is quite time-consuming, it is a compelling challenge to accelerate this procedure. However, the current solutions usually have a number of redundant computations which will significantly affect the efficiency. To address such drawbacks, after an in-depth analysis of skycube computation procedure, we introduce a partial skycube, which only consists of the skylines with frequently used combinations of QoWS. Then the computational relationships between the skyline on one subspace and its parent-space are studied. Based on the relationships, we develop ${\sf {ParCube}}$ ParCube algorithm to speedup partial skycube computation by reusing the intermediate comparison results. Meanwhile, at the execution phase, ${\sf {ParCube}}$ ParCube can be further optimized with parallel execution mode and optimized scheduling strategy. Finally, we evaluate the efficiency and scalability of ${\sf {ParCube}}$ ParCube on both single machine and cluster environment. The results show that ${\sf {ParCube}}$ ParCube can efficiently compute partial skycube and scale well in cluster environment.

In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 &times, 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8.

How to achieve fast and efficient resource allocation is an important optimization problem of resource management in cloud data center. On one hand, in order to ensure the user experience of resource requesting, the system has to achieve fast resource allocation to timely process resource requests; on the other hand, in order to ensure the efficiency of resource allocation, how to allocate multi-dimensional resource requests to servers needs to be optimized, such that server's resource utilization can be improved. However, most of existing approaches focus on finding out the mapping of each specific resource request to each specific server. This makes the complexity of resource allocation problem increases with the size of data center. Thus, these approaches cannot achieve fast and efficient resource allocation for large-scale data center. To address this problem, we propose a pattern based resource allocation mechanism based on the following findings. In a real-world cloud environment, the resource requests are usually classified into limited types. Thus, the mechanism first utilizes this feature to generate pattern information, which indicates which types of resource requests are suitable to be allocated together to a server. Then, the mechanism uses the pattern information as guidelines to make fast resource allocation decision and fully utilize server's multidimensional resources. Simulation experiments based on real and synthetic traces have shown that our mechanism significantly improves system's resource utilization and reduces the overall number of used servers.

This contribution presents quasi-static tests of three modular prefabricated composite beam-column interior joints (MPCIJs), analyzing the strain distribution, ductility, energy consumption, strength, and stiffness degradation . Three finite element models were established by ABAQUS and compared with quasi-static test results to verify their correctness. The stress mechanism of different materials in MPCIJs was further analyzed. Results showed that the absolute value of the plastic drift ratio was 6.41–8.01% rad, the ductility coefficient was 3.50–4.50, and the equivalent viscous damping coefficient was 0.32–0.33. The overall degradation performance of MPCIJs strength and stiffness were relatively stable. MPCIJs achieved good seismic performance and large rotational deformation capacity. The stress (strain) in the joint module, consistent with the ‘diagonal compression bar’, was diagonally distributed. The high-stress connection bolt at the column end is located on both sides of the centerline along the direction of the beam length. The internal stress of concrete follows an antisymmetric distribution along the diagonal direction and diffuses outward at an angle of 45°.

In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. When considering VMs' dynamically changing resource demands, one hot research topic revolves around reusing VM migration resources more efficiently. The challenge here is finding the best tradeoff between the VM migration optimization performance and complexity. On one hand, to improve the migration efficiency, VM migration planning is adopted to achieve efficient resource reuse while minimizing the number of VM migrations. On the other hand, the huge number of PMs and VMs in a cloud datacenter often adds considerable complexity to migration planning, which hampers the decision-making process in VM migration. To address these issues, this paper proposes a hybrid resource pool model to reduce the complexity of VM migration planning by limiting the scope of VM migration decisions. Then, based on this model, we use our novel resource-reuse optimization mechanism (called HirePool) to improve efficiency by maximizing resource usage with only a few VM migrations. Finally, we perform simulation tests and actual experiments running on a real cloud platform to evaluate HirePool. Results show that HirePool improves average resource usage by 13%, saves the number of PMs used by 12%, and reduces the average number of migrations (compared with contrast mechanisms) by 31%.

We conduct a comparative analysis of two energy efficiency indicators for China: heating value energy intensity (HEI) and economic value energy intensity (MEI). We formulate 1997–2002–2007–2012 hybrid energy comparable sequence use tables in an input-output accounting framework, and compare the two indicators using a randomized block ANOVA. The results show that MEIs and HEIs have significantly different variability patterns among sectors and are evolutionarily divergent over time. The directional changes in MEI and HEI are found to be inconsistent at both the sectoral and national levels. A further analysis with a LMDI index decomposition model shows that the difference between HEI LMDI and HEI LMDI is principally caused by energy prices. Based on the evidence from the two indicators and their relationship to energy prices, we are unconvinced about China's purported improvements in energy efficiency in recent years.

The effect of nanofluids on thermal performance of the miniature heat pipe radiator which was assembled by two heat pipes containing 0.6 vol.% SiO2/water nanofluids and 30 pieces of rectangular aluminum fins was investigated experimentally and theoretically. The wall temperatures of the miniature heat pipe and fin surface temperatures were measured. Results showed that the utilization of SiO2/water nanofluids as a working fluid in the heat pipe enhanced the heat performance by reducing wall temperature differences. Compared with Deionized water (DI water), the thermal resistance of the miniature heat pipe with SiO2/water nanofluids decreased by about 23% to 40%. Furthermore, the theoretical calculation on a basis of one dimension found that the fin heat dissipation in the miniature heat pipe radiator charged SiO2/water nanofluids was about 1.17 times of that of the DI water radiator.

With the popularization and development of cloud computing, lots of scientific computing applications are conducted in cloud environments. However, current application scenario of scientific computing is also becoming increasingly dynamic and complicated, such as unpredictable submission times of jobs, different priorities of jobs, deadlines and budget constraints of executing jobs. Thus, how to perform scientific computing efficiently in cloud has become an urgent problem. To address this problem, we design an elastic resource provisioning and task scheduling mechanism to perform scientific workflow jobs in cloud. The goal of this mechanism is to complete as many high-priority workflow jobs as possible under budget and deadline constraints. This mechanism consists of four steps: job preprocessing, job admission control, elastic resource provisioning and task scheduling. We perform the evaluation with four kinds of real scientific workflow jobs under different budget constraints. We also consider the uncertainties of task runtime estimations, provisioning delays, and failures in evaluation. The results show that in most cases our mechanism achieves a better performance than other mechanisms. In addition, the uncertainties of task runtime estimations, VM provisioning delays, and task failures do not have major impact on the mechanism's performance.

With the popularity of cloud computing technology, service hosting is used as a typical model to deploy different kinds of services on cloud platform. In recent years, how to effectively provide resources for service hosting has attracted more and more attention. However, most of the existing works only focused on how to effectively provide virtual machines for service hosting. They ignored how to efficiently place these virtual machines into physical servers, when considering multidimensional resource requirements. This may result in unreasonable virtual machine placement in servers, thereby causing the underutilization of resource. To address this problem, we propose a novel resource provisioning method including virtual machine provisioning for hosting service and virtual machine placement in servers. The proposed method decides how many virtual machines should be provided for each service by utilizing queuing theory. Then based on the virtual machines to be provided, the proposed method models the virtual machine placement problem as a variant of cutting stock problem, and decides how many servers should be provided by solving this problem. The proposed method is evaluated by simulations. Experimental results show the proposed method achieves a better performance than these baseline methods.