Importance

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Abstract

Many of the existing approaches for grouping of jobs for processing are manual in nature, and are based on intuition. Other approaches involve modeling batches as node-edge graphs and use existing graph theory solutions such as connected components or cliques to find logical subgraphs. However, this approach isn't efficient due to the fact that there may be various constraints unique to batch systems. The disclosure herein provides a method and system for creating logical groups of jobs meeting objective function and satisfying constraints. In this method, a logical grouping of jobs is performed such that all defined constraints are satisfied. If any job is not satisfying one or more of the defined constraints, smaller components of the jobs are derived such that the smaller components satisfy the constraints. The logical groups are formed by merging the jobs and the smaller components satisfying the constraints.

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First Claim