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Baek, Woongki
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Machine-Learning Based Performance Estimation for Distributed Parallel Applications in Virtualized Heterogeneous Clusters

Author(s)
Kim, SeontaePham, NguyenBaek, WoongkiChoi, Young-Ri
Issued Date
2017-06-05
DOI
10.1109/ICDCS.2017.310
URI
https://scholarworks.unist.ac.kr/handle/201301/35316
Fulltext
http://ieeexplore.ieee.org/document/7980252/
Citation
IEEE International Conference on Distributed Computing Systems
Abstract
In a virtualized heterogeneous cluster, for a distributed parallel application which runs in multiple virtual machines (VMs) concurrently, there are a huge number of possible ways to place its VMs. This paper investigates a performance estimation technique for distributed parallel applications in virtualized heterogeneous clusters. We first analyze the effects of different VM configurations on the performance of various distributed parallel applications. We then present a machine-learning based performance model for a distributed parallel application. Using a heterogeneous cluster with two different types of nodes, we show that our machine-learning based models can estimate the runtimes of distributed parallel applications with modest error rates.
Publisher
IEEE

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