ANANKE: a Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows: Technical Report DS-2017-001

Shenjun Ma, Alexey Ilyushkin, Alexander Stegehuis, Alexandru Iosup

Research output: Book/ReportReportScientific

113 Downloads (Pure)


Complex workflows that process sensor data are useful for industrial infrastructure management and diagnosis. Although running such workflows in clouds promises reduces operational costs, there are still numerous scheduling challenges to overcome. Such complex workflows are dynamic, exhibit periodic patterns, and combine diverse task groupings and requirements. In this work, we propose ANANKE, a scheduling system addressing these challenges. Our approach extends the state-of-the-art in portfolio scheduling for datacenters with a reinforcement-learning technique, and proposes various scheduling policies for managing complex workflows. Portfolio scheduling addresses the dynamic aspect of the workload. Reinforcement learning, based in this work on Q-learning, allows our approach to adapt to the periodic patterns of the workload, and to tune the other configuration parameters. The proposed policies are heuristics that guide the provisioning process, and map workflow tasks to the provisioned cloud resources. Through real-world experiments based on real and synthetic industrial workloads, we analyze and compare our prototype implementation of ANANKE with a system without portfolio scheduling (baseline) and with a system equipped with a standard portfolio scheduler. Overall, our experimental results give evidence that a learningbased portfolio scheduler can perform better (5–20%) and cost less (20–35%) than the considered alternatives.
Original languageEnglish
Place of PublicationDelft, the Netherlands
PublisherDelft University of Technology
Number of pages36
Publication statusPublished - 2017


Dive into the research topics of 'ANANKE: a Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows: Technical Report DS-2017-001'. Together they form a unique fingerprint.

Cite this