A Control Theoretic Approach for Workflow Management

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.