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Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker.

Several solar irradiation forecasting methods are being developed toward economically efficient solar power generation. Among these, the method that utilizes all sky imagers to estimate the direction of cloud movement above certain observation points is able to forecast fluctuations in solar irradiation with localized temporal resolution (∼30 min) through observation of shadows cast by clouds. The one-layered cloud is usually assumed in forecasting the direction of cloud movement. However, in reality, multilayered clouds may move in different directions in the sky. While such multilayered clouds may have effect on forecast accuracy, the extent to which multilayered clouds affect the solar irradiation and the generated solar power has never been reported. Therefore, the purposes of this study are (1) to give the dataset to know the effect of multilayered clouds and (2) to decide whether the multilayered clouds have to be considered in forecasting of solar irradiation. We analyzed the number of cloud layers and their effect on the solar irradiation, the generated solar power, and the clearness index through statistical analysis for all seasons in Toyohashi city, Japan. The dataset used to analysis includes data with 4381 h. The analytical results show that multilayered clouds were observed for ∼40% of the daytime duration even in winter. In addition, the results show that multilayered clouds were observed even when the solar irradiation, the generated solar power, and the clearness index were high, which suggests that the effect of multilayered clouds must be considered. In addition, it will be shown that the maximum number of cloud layers to be considered is 3 from statistical analysis.

The wide adoption of cloud computing by businesses is due to several reasons, among which the elasticity of the cloud virtual infrastructure is the definite leader. Container technology allows increasing the flexibility of an application by adding another layer of virtualization. The containers can be dynamically created and terminated, and also moved from one host to another. A company can achieve a significant cost reduction and increase the manageability of its applications by allowing the running of containerized microservice applications in the cloud. Scaling for such solutions is conducted on both the virtual infrastructure layer and the container layer. Scaling on both layers needs to be synchronized so that, for example, the virtual machine is not terminated with containers still running on it. The synchronization between layers is enabled by multilayered cooperative scaling , implying that the autoscaling solution of the virtual infrastructure layers is aware of the decisions of the autoscaling solution on the container layer and vice versa. In this paper, we introduce the notion of cooperative multilayered scaling and the performance of multilayered autoscaling solutions evaluated using the approach implemented in ScaleX (previously known as Autoscaling Performance Measurement Tool, APMT). We provide the results of the experimental evaluation of multilayered autoscaling performance for the combination of virtual infrastructure autoscaling of AWS, Microsoft Azure and Google Compute Engine with pods horizontal autoscaling of Kubernetes by using ScaleX with four distinct load patterns. We also discuss the effect of the Docker container image size and its pulling policy on the scaling performance.

To meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multilayered neural network, which is trained by using back-propagation (BP) algorithm based on Map-Reduce of cloud computing clusters. The batch-training (or epoch-training) regimes are used by effective segmentation of samples on the clusters, and are adopted in the separated training method, weight summary to achieve convergence by iterating. For a parallel BP algorithm on the clusters and a serial BP algorithm on an uniprocessor, the required time for implementing the algorithms is derived. The performance parameters, such as speedup, optimal number and minimum of data nodes are evaluated for the parallel BP algorithm on the clusters. Experiment results demonstrate that the proposed parallel BP algorithm in this paper has better speedup, faster convergence rate, less iterations than that of the existed algorithms.