Stephan Schlagkamp

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The performance of parallel schedulers is a crucial factor in the efficiency of high performance computing environments. Scheduler designs for practical application focusing on improving certain metrics can only be achieved, if they are evaluated in realistic testing environments. Since real users submit jobs to their respective system, special attention(More)
Understanding user behavior is crucial for the evaluation of scheduling and allocation performances in HPC environments. This paper aims to further understand the dynamic user reaction to different levels of system performance by performing a comprehensive analysis of user behavior in recorded data in the form of delays in the subsequent job submission(More)
In this paper, we investigate the differences and similarities in user job submission behavior in High Performance Computing (HPC) and High Throughput Computing (HTC). We consider job submission behavior in terms of parallel batch-wise submissions, as well as delays and pauses in job submission. Our findings show that modeling user-based HTC job submission(More)
The performance evaluation of parallel computing environments is crucial for the design of parallel job schedulers, as well as policy definitions. The analysis of user behavior is fundamental to unveil individual behaviors and reactions to different system performances (e.g., scarce resources, low throughput, etc.). In this paper, we present an analysis of(More)
Scheduling of jobs in parallel computing is crucial to efficiently use shared resources, while attaining user satisfaction. In this paper, we evaluate how mixed-integer linear programming (MILP) can be applied for the online parallel job scheduling problem (which is well-known to be an NP-complete problem). Therefore, we introduce the idea of planning(More)
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