International Business Machines Corporation

Bhooshan Prafulla Kelkar

Sandeep R Patil

Prashant Sodhiya

Mahesh Shivram Paradkar

Riyazahamad Moulasab Shiraguppi

Abhinay Ravinder Nagpal

Deepak A Deshpande

Rajiv D Bendale

William Earl Malloy

Frederick Davis Busche

Sanjay Dinkar Bhat

Riyaz M Shiraguppi

Sneha M Mone

A method, computer program product, and system for human resource analytics using profile data is described. The method includes receiving historical project data, wherein the historical project data includes at least one profile of at least one historical project team member. Psychometric data associated with the at least one historical project team member is received. Updated historical project data is generated, in which, at least in part, psychometric data associated with the at least one historical project team member is added to the at least one profile included in the historical project data.

Human resource analytics with profile data

The present invention relates to a method, system, and computer program product for determining storage device weight values to use to select one of the storage devices to use as a target storage to which data from a source storage is migrated. A determination is made, for each of the storage devices, of static parameter values for static parameters comprising attributes of the storage device and dynamic parameter values for dynamic parameters providing device health information determined by accessing the storage device to determine operational conditions at the storage device. Storage device weight values are determined as a function of the static parameter values and the dynamic parameter values of the device. The determined storage device weight values are used to select one of the storage devices as the target storage to which data from the source storage is migrated.

Determining weight values for storage devices in a storage tier to use to select one of the storage devices to use as a target storage to which data from a source storage is migrated

Exemplary embodiments of the present invention disclose a method, computer program product, and system for optimizing a clustered virtual computing environment. In exemplary embodiments, performance attributes are identified for a set of operating devices within the clustered virtual computing environment. Historical data of the identified performance attributes is obtained to create a historical data repository. A rulebase is developed using the historical data repository and input from user. A combined correlation pattern repository is generated using a first correlation pattern, a second correlation pattern and a scale-time invariant weight fraction.

Optimizing a clustered virtual computing environment

Embodiment of the invention related to characterization and optimization of tracks on a disk, magnetic or optical by determining an input/output (I/O) characteristics for a plurality of blocks on a disk by a processor, wherein the characteristics comprise at least one of a data size or data type or an association between the data files, and determining a plurality of parameters affecting operation performed on the disk for placement of the plurality of data clusters.

Characterization and optimization of tracks on disks

A method, system, and computer program product for mitigating adjacent track erasures in hard disks, includes: determining input/output (I/O) characteristics for a plurality of blocks on a hard disk; assigning the plurality of blocks to a plurality of categories of I/O characteristics by the processor; and clustering content of the blocks assigned to the same category in one or more continuous tracks on the hard disk. Each block is assigned to one category. Blocks with similar I/O characteristics are clustered on one or more continuous tracks. By performing this clustering, blocks with a high number of I/O operations are grouped and stored on fewer tracks than if they were scattered across numerous tracks. This reduces the number of tracks experiencing a high number of I/O operations, and in turn, the amount of refreshing of adjacent tracks is reduced.

Method and system for mitigating adjacent track erasure in hard disk drives

Optimization of tracks on a hard disk includes: determining I/O characteristics for data clusters to be stored on the hard disk; generating a set of solutions for each possible placement configuration; for each solution, calculating a plurality of cost functions using the I/O characteristics for the plurality of data clusters; for each solution, calculating a membership value using the cost function values; for each solution, calculating a fitness value using the membership value; retaining the solutions with the fitness value greater than a predetermined threshold; determining whether at least one stopping condition has been met; if not, adding new solutions to the retained solutions to generate the next set of solutions; and repeating the calculating the membership value, the calculating the fitness value, and the retaining the solutions with the fitness value greater than the predetermined threshold until at least one stopping condition has been met.

Smart optimization of tracks for cloud computing

A system and computer readable medium for discovering gene regulatory models using relational fuzzy models. A system is provided that includes a data selection system that clusters gene expression data into a set of clusters and identifies a representative subset of genes from the set of clusters; and a relational fuzzy modeling system that builds a relational fuzzy model using the representative subset.

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Pune, India

Bhooshan Prafulla Kelkar