Abstract: Systems, apparatuses and methods provide technology that receives different numbers of accesses to a data shard from different regions, where the data shard is a portion of a dataset. The technology identifies, with a machine learning model, access patterns based on the different numbers of accesses, and generates, with the machine learning model, values for the different regions based on the access patterns, where the values represent ranks of the different regions that correspond to a future number of predicted accesses to the data shard from the different regions. The technology determines a subset of the different regions to store the data shard based on the values and stores a replica of the data shard in each of the subset of the regions without scarifying latency.

Abstract: Disclosed are techniques that provide for deduplication in an efficient and effective manner. For example, such methods, computer program products, and computer systems can include retrieving container information for a first one or more containers of a plurality of containers of one or more backup images (where the one or more backup images were produced under an existing backup policy), generating pre-processed container information (where the generating the pre-processed container information comprises performing data pre-processing on the container information), determining a plurality of container ranges for the first one or more containers, generating container range affinity information for the one or more backup images (where the generating the container range affinity information comprises performing a container range operation using the plurality of container ranges, and storing the container range affinity information in a container range data structure.

Abstract: Methods, computer program products, computer systems, and the like are disclosed that provide for improved deduplication performance using prefetched backup information. For example, such methods, computer program products, and computer systems can include generating new feature information for a new backup image, (for each existing backup image in a plurality of existing backup images) comparing the new feature information with existing feature information for the each existing backup image, identifying one or more existing backup images of the plurality of existing backup images, and prefetching the one or more existing backup images. The feature information is generated as an output of a machine learning process. The machine learning process receives at least a portion of data of the new backup image as an input. The one or more existing backup images are identified based, at least in part, on a result of the comparing.

Abstract: Disclosed are techniques that provide for deduplication in an efficient and effective manner. For example, such methods, computer program products, and computer systems can include generating new feature information for one or more portions of a new backup image, generating first container range information by performing a container range calculation using the new feature information, generating existing feature information for one or more portions of an existing backup image, generating second container range information by performing the container range calculation using the existing feature information, determining a container range affinity between the first container range information and the second container range information, identifying at least one portion of the one or more portions of the existing backup image using a result of the determining, and prefetching the one or more fingerprints corresponding to the at least one portion of the one or more portions of the existing backup image.

Abstract: Disclosed are techniques that provide for deduplication in an efficient and effective manner. For example, such methods, computer program products, and computer systems can include retrieving container information for a first one or more containers of a plurality of containers of one or more backup images (where the one or more backup images were produced under an existing backup policy), generating pre-processed container information (where the generating the pre-processed container information comprises performing data pre-processing on the container information), determining a plurality of container ranges for the first one or more containers, generating container range affinity information for the one or more backup images (where the generating the container range affinity information comprises performing a container range operation using the plurality of container ranges, and storing the container range affinity information in a container range data structure.

Abstract: Provided is a nano antibody, the amino acid sequence thereof being EVQLQASGGGFVQPGGSLRLSCAASGFTFSSX1AMGWFRQAPGKEREX2VSAISSGGGNTYYADSVKGRFTISRDNSKNTVYLQMNSLRAEDTATYYCVTPGGRLWYYRYDYRCQGTQVTVSS (SEQ ID NO: 1), where X1 is selected from Y or F, and X2 is selected from F or L. The antibody can be used to dissolve Charcot-Leyden crystals (CLCs), thereby reducing pulmonary inflammation, changes in lung function, and mucus production. Further provided is the use of the nano antibody in the preparation of a drug and a reagent for testing Charcot-Leyden crystals (CLCs) and/or Galectin-10 protein.