Abstract: A computer-implemented method, a system and a computer program product for device failure detection are disclosed. In the method, phase-based predictions may be performed on a plurality of storage devices to determine a plurality of sampling scopes and corresponding sampling ratios. The respective sampling scopes may comprise at least one storage device of the plurality of storage devices. A sampling dataset may be obtained by selecting a group of storage devices from the respective sampling scopes with the corresponding sampling ratios. Device failure may be detected for the group of storage devices based on the sampling dataset.

Abstract: Automatic container image registry selection is provided. A predefined number of top performing container image registries is selected from a top of a sorted list of a plurality of container image registries listed in descending order. A cluster of host nodes where a container is deployed is enabled to pull a same container image corresponding to the container utilizing different predefined pulling percentages from the predefined number of top performing container image registries. The cluster of host nodes utilizes a different predefined pulling percentage to pull the same container image from each respective top performing container image registry. A current real time average pulling speed of the cluster of host nodes for the same container image from each respective top performing container image registry of the predefined number of top performing container image registries is recorded over a defined time period.

Abstract: One aspect of the present invention relates to a new approach to the demosaicing of spatially sampled image data observed through a color filter array. In one embodiment properties of Smith-Barnwell filterbanks may be employed to exploit the correlation of color components in order to reconstruct a sub-sampled image. In other embodiments, the approach is amenable to wavelet-domain denoising prior to demosaicing. One aspect of the present invention relates to a framework for applying existing image denoising algorithms to color filter array data. In addition to yielding new algorithms for denoising and demosaicing, in some embodiments, this framework enables the application of other wavelet-based denoising algorithms directly to the CFA image data.