An Enhanced Mathematical Model For Cloud Based Data Oriented Job Analysis

Abstract

Data processing or data analytics is the common functionality that is attached to most of the real world applications. The amount of processing required for data oriented tasks or jobs are quiet high. To resolve the processing issue the most common approach deployed is by using a high performance cluster. Setting a cluster over real time infrastructure leads to a very expensive solution. A Cloud based infrastructure remains as an ideal support for setting up a cluster. Managing the cluster over a Cloud is a challenging task as allocation of infrastructure based on the task schedule is a critical parameter. The proposed mathematical model introduces a strategy to allocate the infrastructure and manage the load of the cluster based on Queuing model. The experimental setup is made on top of private cloud and Hadoop based data processing jobs are tested. The proposed data oriented resource optimizer enhances the performance of the cluster by balancing the increased load due to data processing jobs. The result shows the enhanced improvement in performance compared to default resource manager.