Transfer Learning-Based Co-Run Scheduling for Heterogeneous Datacenters


  • Wei Kuang Michigan Technological University
  • Laura Brown Michigan Technological University
  • Zhenlin Wang Michigan Technological University



Transfer Learning, Regression Modeling


Today’s data centers are designed with multi-core CPUs where multiple virtual machines (VMs) can be co-located into one physical machine or distribute multiple computing tasks onto one physical machine. The result is co-tenancy, resource sharing and competition. Modeling and predicting such co-run interference becomes crucial for job scheduling and Quality of Service assurance. Co-locating interference can be characterized into two components, sensitivity and pressure, where sensitivity characterizes how an application’s own performance is affected by a co-run application, and pressure characterizes how much contentiousness an application exerts/brings onto the memory subsystem. Previous studies show that with simple models, sensitivity and pressure can be accurately characterized for a single machine. We extend the models to consider cross-architecture sensitivity (across different machines).




How to Cite

Kuang, W., Brown, L., & Wang, Z. (2015). Transfer Learning-Based Co-Run Scheduling for Heterogeneous Datacenters. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).