Abstract: Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.

Abstract: Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.

Abstract: The present disclosure relates to systems and methods that enhance log data processing, storage, and querying by dynamically allocating mapping connections between processing/storage components based on characteristics of incoming data and the processing/storage components. More particularly, incoming data from a data stream may be extracted and utilized according to dynamic component mapping to more efficiently process the data. Additionally, active processing/storage metrics may cause dynamic reallocation of the component mappings to balance resource loads.

Abstract: A computer-implemented method may include availing a computer platform to a client system. The computer platform may host data and perform data processing. The computer platform may receive a data set from the client system. Computational resources may be determined for the client system, based at least in part on the size of the data set and a size of other data received from the client system, and a category assigned to the client system. A weight may be determined for the client system based on the category, indicating a degree to which resources may be preferentially allocated to process requests associated with the client system. The client system may request a data operation be performed using the data set. Allocation of one or more resources may be prioritized for the data operation, based on the weight. The prioritization may influence how quickly the data operation is performed.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. The log analytics system, method, and computer program product provide target-based configuration of log monitoring metadata. The log analytics system, method, and computer program product can be implemented in a cloud-based/SaaS-based architecture.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. In addition, a method and system is provided for implementing collection-wise processing within a log analytics system.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. The log analytics system, method, and computer program product provide target-based configuration of log monitoring metadata. The log analytics system, method, and computer program product can be implemented in a cloud-based/SaaS-based architecture.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. The log analytics system, method, and computer program product provide target-based configuration of log monitoring metadata. The log analytics system, method, and computer program product can be implemented in a cloud-based/SaaS-based architecture.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. In addition, a method and system is provided for implementing collection-wise processing within a log analytics system.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. The log analytics system, method, and computer program product provide target-based configuration of log monitoring metadata. The log analytics system, method, and computer program product can be implemented in a cloud-based/SaaS-based architecture.

Abstract: A method, system, and computer program product for automated data center platform consolidation. The method commences defining a set of source platforms (e.g., servers, database machines, network infrastructure components, etc.) where the source platforms are associated with time-variant demand models (e.g., computing demand during the working/daytime periods, demand at night, etc.). The demand models can be measured or estimated, and can be in any units of time (e.g., days, hours, etc.). Then a set of target platforms is defined where a member of the set of target platforms has a respective target platform profile comprising an initial time-variant availability model to compare with the time-variant demand models of the source platforms. Time-variant constraints (e.g., not more than 80% utilization during working/daytime periods, etc.) are observed while mapping members of the set of source platforms to the members of the set of target platforms until a consolidation stopping condition is reached.

Abstract: A method, system, and computer program product for automated data center platform consolidation. The method commences defining a set of source platforms (e.g., servers, database machines, network infrastructure components, etc.) where the source platforms are associated with time-variant demand models (e.g., computing demand during the working/daytime periods, demand at night, etc.). The demand models can be measured or estimated, and can be in any units of time (e.g., days, hours, etc.). Then a set of target platforms is defined where a member of the set of target platforms has a respective target platform profile comprising an initial time-variant availability model to compare with the time-variant demand models of the source platforms. Time-variant constraints (e.g., not more than 80% utilization during working/daytime periods, etc.) are observed while mapping members of the set of source platforms to the members of the set of target platforms until a consolidation stopping condition is reached.