Jumbune's 'Job Optimization' is a proprietary framework with an in-built cost based optimization algorithm that performs tuning of the analytical jobs on hadoop cluster. The optimization is iterative and is based on the application at hand and predicts the best configuration with respect to the application, cluster load and behavior.
There are multiple optimization profiles that aid in improved prediction for jobs of varying workload, I/O and data size. The time bound optimization helps administrators, who are on strict deadlines to use the optimization framework in a fixed time frame. This makes sure that the optimization is performed during off-peak hours and it doesn't interfere with the normal job execution schedule on the cluster.
During each passing iteration Jumbune's optimization framework makes intelligent changes to the configuration which are based on it's adaptive learning from previous iterations and stops once the optimizations have plateaued. The configuration can be exported as XML files or command line arguments. Jumbune also has an offline tuning mode that calculates optimal configuration based on the job's history. This feature the ability to improve HIVE/Pig jobs execution times. The user just has to mention the job id of the job and Jumbune would predict a better optimized configuration.