Tata Consultancy Services Limited

Muralidharan Somasundaram

Natarajan Vijayarangan

Kishore Padmanabhan

Mahesh M Kshirsagar

Sumit Raut

Sumukh Sudhakar Sirmokadam

Sumit Inderjeet Juneja

Method and system for enabling dynamic capacity planning are disclosed. In an embodiment, a plurality of operational inputs are configured to forecast a demand volume and generate a dynamic capacity planning schedule. For example, said plurality of operational inputs include raw data received from a server. Further, historical data is processed based on said plurality of operational inputs to create a plurality of contextual data elements. Furthermore, said demand volume is forecasted and said dynamic capacity planning schedule is generated using at least a part of the historical data and a current capacity planning requirement, said current capacity planning requirement is being generated dynamically by said server. In addition, said dynamic capacity planning schedule is rendered based on said forecasted demand volume and said rendered dynamic capacity planning schedule is clustered into a plurality of work reports associated with said plurality of contextual data elements.

Method and system for enabling dynamic capacity planning

Method and system for determining the number of computing resources for executing a plurality of jobs. In accordance with the method and the system, the plurality of jobs along with a plurality of variables associated to the plurality of jobs may be received. The plurality of variables may include a mean processing time (&#x3bc;), a standard deviation (&#x3c3;), a service level agreement (&#x3b1;), and a confidence level (1-&#x3b1;). After the receipt of the plurality of variables, an inverse parameter (&#x3b3;) may be determined using the mean processing time (&#x3bc;), the standard deviation (&#x3c3;), and the confidence level (1-&#x3b1;). Further, a queue length (k) may be computed based upon the total processing time (T), the mean processing time (&#x3bc;), the standard deviation (&#x3c3;), and the inverse parameter (&#x3b3;). Finally, the queue length (k) and the number of jobs (n) may be processed in order to determine the number of computing resources.

System and method for determining number of computing resources for executing a plurality of jobs

Method and system for determining the total processing time required for executing a plurality of jobs. In certain aspects, a set of jobs along with a mean processing time and a queue length are received. The set of jobs are distributed across computing resources based upon the queue length. A set of priority jobs is received when the execution of a subset of jobs from the set of jobs has been executed in a first processing time (T). Further, the set of priority jobs is distributed for execution across the plurality of computing resources. A second processing time (T) required for the execution of the set of priority jobs is computed. Further, a third processing time (T) required for the execution of the set of remaining jobs from the set of jobs is determined. Finally, the total processing time is determined by summing T, T, and T.

System and method for determining total processing time for executing a plurality of jobs

Method and system for determining the total processing time required for executing a plurality of jobs (n). The plurality of jobs along with a mean processing time (&#x3bc;), and a queue length (k) are received. A number of preliminary jobs (p) of the plurality of jobs (n) are distributed across computing resources based upon the queue length (k). A first processing time (T) required for the execution of the number of preliminary jobs (p) is determined. Further, a number of remaining jobs (n&#x2212;p) of the plurality of jobs (n) are distributed for execution across the plurality of computing resources. A second processing time (T) required for the execution of the set of remaining jobs (n&#x2212;p) is computed. Finally, the total processing time is determined by summing Tand T.

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Tamil Nadu, India

Muralidharan Somasundaram