Flowcon: elastic flow configuration for containerized deep learning applications
Zheng, Wenjia, Tynes, MichaelORCID: 0000-0002-5007-1056, Gorelick, Henry, Mao, Ying, Cheng, LongORCID: 0000-0003-1638-059X and Hou, YantianORCID: 0000-0001-8295-6871
(2019)
Flowcon: elastic flow configuration for containerized deep learning applications.
In: Proceedings of the 48th International Conference on Parallel Processing, 5 -8 Aug 2019, Kyoto, Japan.
ISBN 978-1-4503-6295-5
An increasing number of companies are using data analytics to
improve their products, services, and business processes. However,
learning knowledge effectively from massive data sets always involves nontrivial computational resources. Most businesses thus
choose to migrate their hardware needs to a remote cluster computing service (e.g., AWS) or to an in-house cluster facility which
is often run at its resource capacity. In such scenarios, where jobs
compete for available resources utilizing resources effectively to
achieve high-performance data analytics becomes desirable. Although cluster resource management is a fruitful research area
having made many advances (e.g., YARN, Kubernetes), few projects
have investigated how further optimizations can be made specifically for training multiple machine learning (ML) / deep learning
(DL) models. In this work, we introduce FlowCon, a system which
is able to monitor loss functions of ML/DL jobs at runtime, and thus
to make decisions on resource configuration elastically. We present
a detailed design and implementation of FlowCon, and conduct
intensive experiments over various DL models. Our experimental
results show that FlowCon can strongly improve DL job completion time and resource utilization efficiency, compared to existing
approaches. Specifically, FlowCon can reduce the completion time
by up to 42.06% for a specific job without sacrificing the overall
makespan, in the presence of various DL job workloads.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
cloud computing; deep learning; containerized application; resource
management; high performance analytics