Print Email Facebook Twitter ANANKE: a Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows Title ANANKE: a Q-Learning-Based Portfolio Scheduler for Complex Industrial Workflows: Technical Report DS-2017-001 Author Ma, S. (TU Delft Dataintensive Systems) Ilyushkin, A.S. (TU Delft Dataintensive Systems) Stegehuis, Alexander (Shell) Iosup, A. (TU Delft Dataintensive Systems; Vrije Universiteit Amsterdam) Date 2017 Abstract 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. To reference this document use: http://resolver.tudelft.nl/uuid:2e128806-0a83-41b6-8f14-e3c582ef81a3 Publisher Delft University of Technology, Delft, the Netherlands Part of collection Institutional Repository Document type report Rights © 2017 S. Ma, A.S. Ilyushkin, Alexander Stegehuis, A. Iosup Files PDF DS_2017_001.pdf 1.5 MB Close viewer /islandora/object/uuid:2e128806-0a83-41b6-8f14-e3c582ef81a3/datastream/OBJ/view