Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data-intensive Grid applications need access to large data sets that may each be replicated on different resources. Minimizing the overhead of transferring these data sets to the resources where the applications are executed requires that appropriate computational and data resources be selected. ,  consider the problem of scheduling an application composed of a set of independent tasks, each of which requires multiple data sets that are each replicated on multiple resources.  break this problem into two parts: one, to match each task (or job) to one compute resource for executing the job and one storage resource each for accessing each data set required by the job and two, to assign the set of tasks to the selected resources.  model the first part as an instance of the ll-known Set Covering Problem (SCP) and apply a known heuristic for SCP to match jobs to resources. The second part is tackled by extending existing MinMin and Sufferage algorithms to schedule the set of distributed data-intensive tasks. Through simulation,  experimentally compare the SCP-based matching heuristic to others in conjunction with the task scheduling algorithms and present the results.
['Srikumar Venugopal', 'Rajkumar Buyya']
I am writing a research paper on a idea An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids
[{"from": "human", "value": "I am writing a research paper on a idea An SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data-intensive Grid applications need access to large data sets that may each be replicated on different resources. Minimizing the overhead of transferring these data sets to the resources where the applications are executed requires that appropriate computational and data resources be selected. ,  consider the problem of scheduling an application composed of a set of independent tasks, each of which requires multiple data sets that are each replicated on multiple resources.  break this problem into two parts: one, to match each task (or job) to one compute resource for executing the job and one storage resource each for accessing each data set required by the job and two, to assign the set of tasks to the selected resources.  model the first part as an instance of the ll-known Set Covering Problem (SCP) and apply a known heuristic for SCP to match jobs to resources. The second part is tackled by extending existing MinMin and Sufferage algorithms to schedule the set of distributed data-intensive tasks. Through simulation,  experimentally compare the SCP-based matching heuristic to others in conjunction with the task scheduling algorithms and present the results."}]