International Business Machines Corporation

Binayak Dutta

Sandeep R Patil

Harish Bharti

Sangeetha Seshadri

Abhay Kumar Patra

Umesh Deshpande

Thanh Tan Pham

A computer-implemented method (CIM), according to one embodiment, includes simulating placement of a volume group on a first potential target storage system, and determining a first performance limit for the first potential target storage system based on the simulating. The method further includes predicting a duration the first potential target storage system can host the volume group without performance degradation, based on the first performance limit, and outputting a notification to a customer device, where the notification includes an indication of the predicted duration. A computer program product (CPP), according to another embodiment, includes a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform the foregoing method.

Performance-based migration for a volume group

A method of searching a relational database table which includes: providing a relational database table having a plurality of rows and columns such that each of the plurality of columns has a column name descriptive of data in the each of the columns; providing a relational database management system (RDBMS) to manage access to the relational database table; providing at least one metadata tag to some of the plurality of columns wherein the metadata tag is a non-hierarchical keyword or term that is assigned to the some of the plurality of columns; responsive to selecting at least one metadata tag and responsive to selecting a search string, searching by a search and join function of the RDBMS columns of the some of the plurality of columns corresponding to the at least one metadata tag in the relational database table to find data matching the search string.

Cross column searching a relational database table

Energy load forecasts are generated via model(s) for the grid hierarchy elements for different forecast time scale periods as a function of different sets of prioritized contextual influencing factors for respective associated combinations of grid hierarchy elements and forecast time scale periods. Relative priority values of the sets of the contextual influencing factors are iteratively weighted until a revised energy load forecast generated as a function of the weighted values via the model(s) is within a threshold value of a historic energy load data value for the associated combination of the grid hierarchy element and forecast time scale period.

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Pune, India

Binayak Dutta