Statistical Approach to Optimize Master-Worker Allocation in Grid Computing

We investigate the problem arising in allocating master and workers in the Master-Worker Paradigm. Although various methods have beenproposed for Master-Worker allocation, optimal allocation is yet to be achieved. This paper proposes an extension to the generic Master-Workerarchitecture for achieving optimal allocation of masters and workers. The architecture is extended by introducing an additional utilizing layer inbetween the Grid application layer and the Grid service layer, which is called Master-worker optimization layer. The layer consists of aknowledge base and uses statistical knowledge for utilization of the resource allocation. The salient feature of this layer is that it uses the priorknowledge and numerical data relating to groups of individual computers for the decision making process. The paper describes the architecture ofthe extended Master-Worker architecture; its functionality, possible alternatives for the Master-worker optimization layer and assessment of theproposed method