In this paper we present a framework for developing an intelligent job management and scheduling system that utilizes application specific benchmarks to mould jobs onto available resources. In an attempt to achieve the seemingly irreconcilable goals of maximum usage and minimum turnaround time this research aims to adapt an open-framework benchmarking scheme to supply information to a mouldable job scheduler. In a green IT obsessed world, hardware efficiency and usage of computer systems becomes essential. With an average computer rack consuming between 7 and 25 kW it is essential that resources be utilized in the most optimum way possible. Currently the batch schedulers employed to manage these multi-user multi-application environments are nothing more than match making and service level agreement (SLA) enforcing tools. These management systems rely on user prescribed parameters that can lead to over or under booking of compute resources. System administrators strive to get maximum “usage efficiency” from the systems by manual fine-tuning and restricting queues. Existing mouldable scheduling strategies utilize scalability characteristics, which are inherently 2dimensional and cannot provide predictable scheduling information. In this paper we have considered existing benchmarking schemes and tools, schedulers and scheduling strategies, and elastic computational environments. We are proposing a novel job management system that will extract performance characteristics of an application, with an associated dataset and workload, to devise optimal resource allocations and scheduling decisions. As we move towards an era where on-demand computing becomes the fifth utility, the end product from this research will cope with elastic computational environments
