Titlebook: Job Scheduling Strategies for Parallel Processing; 21st International W Dalibor Klusáček,Walfredo Cirne,Narayan Desai Conference proceeding

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Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties,ll equally benefit other scientific workflows executed on distributed resources with demand uncertainties. The proposed method can also be extended to include uncertainties related to resource availability and network performance.

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Analysis of Mixed Workloads from Shared Cloud Infrastructure,g both “grid jobs” and cloud VMs increase the complexity of required (co)scheduling necessary to efficiently use the underlying physical infrastructure. Second, we also provide a detailed description of the setup of the system, its operational constraints and unresolved issues, putting the observed

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Supporting Real-Time Jobs on the IBM Blue Gene/Q: Simulation-Based Study,, which may have to be preempted. The overhead involved in preempting and restarting batch jobs will, in turn, negatively impact system utilization. Here we evaluate various scheduling schemes to support real-time jobs along with the traditional batch jobs. We perform simulation studies using trace

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ScSF: A Scheduling Simulation Framework,, comparative workload analysis, and experiment orchestration. The simulator is designed to be run over a distributed computing infrastructure facilitating large-scale tests. We demonstrate ScSF through a case study to develop new techniques to manage scientific workflows in a batch scheduler. The e

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DJSB: Dynamic Job Scheduling Benchmark,ent-driven tool. Results show that just changing some DJSB arguments we can set up and execute quite different experiments, making easy the comparison. In this particular case, a cooperative-dynamic resource management is evaluated compared with other resource management approaches.