Abstract
This paper explores the performance of feedback control when managing workflows in computing systems. Industrial systems nowadays can consist of geographically diverse and heterogeneous high-performance computing (HPC) clusters. When scheduling workflows over such platforms, it is often desired to observe a number of real-time objectives such as meeting deadlines, reducing slacks, and increasing platform utilisation. We apply a control theoretic approach to address scheduling-related trade-offs of workflows that are executed in HPC platforms. Our results show that model predictive control-based admission controller is efficient for scheduling periodic workflows in a homogeneous HPC cluster with respect to minimum slacks and maximum CPU utilisation.
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