Transfer learning-based co-run scheduling for heterogeneous dataceners
Document Type
Article
Publication Date
2015
Department
Department of Computer Science; Center for Data Sciences; Center for Scalable Architectures and Systems
Abstract
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).
Publication Title
Twenty-Ninth AAAI Conference on Artificial Intelligence
Recommended Citation
Kuang, W.,
Brown, L. E.,
&
Wang, Z.
(2015).
Transfer learning-based co-run scheduling for heterogeneous dataceners.
Twenty-Ninth AAAI Conference on Artificial Intelligence.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/740
Publisher's Statement
© 2015 Association for the Advancement of ArtificialIntelligence. Publisher's version of record: https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9332