Venugopal R. Chakravarthy

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With the advent of large scale computing environment, there is a need for scheduling methods which allow multiple DAG-structured applications to share the cluster resources with the objectives of increasing the throughput and maximizing the resource utilization. The number of processors allotted to each application is decided during runtime, depending on(More)
Many computational solutions can be expressed as Directed Acyclic Graph (DAG), in which nodes represent tasks to be executed and edges represent precedence constraints among tasks. A Cluster of processors is a shared resource among several users and hence the need for a scheduler which deals with multi-user jobs presented as DAGs. The scheduler must find(More)
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared resource among several users and hence the need for a scheduler which deals with multi-user jobs presented as workflows. The scheduler must find the number of processors to be allotted for each workflow and schedule tasks on allotted processors. In this work, a(More)
Many applications in scientific computations exhibit both data and task parallelism. Several studies have proved that designing parallel applications using both task and data parallelism is an effective approach than pure data or pure task parallel models. This mixed parallelism achieves both higher scalability and performance. Mixed parallel applications(More)
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