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Home » Elastic Memory: Bring Elasticity Back to In-Memory Big Data Analytics
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Elastic Memory: Bring Elasticity Back to In-Memory Big Data Analytics

Monday, May 18, 2015 - 10:00am-10:30am

Joo Seong Jeong, Woo-Yeon Lee, Yunseong Lee, Youngseok Yang, Brian Cho, Byung-Gon Chun, Seoul National University

Abstract: 

Recent big data processing systems provide quick answers to users by keeping data in memory across a cluster. As a simple way to manage data in memory, the systems are deployed as long-running workers on a static allocation of the cluster resources. This simplicity comes at a cost: elasticity is lost. Using today’s resource managers such as YARN and Mesos, this severely reduces the utilization of the shared cluster and limits the performance of such systems. In this paper, we propose Elastic Memory, an abstraction that can dynamically change the allocated memory resource to improve resource utilization and performance. With Elastic Memory, we outline how we enable elastic interactive query processing and machine learning.

Joo Seong Jeong, Seoul National University

Woo-Yeon Lee, Seoul National University

Yunseong Lee, Seoul National University

Youngseok Yang, Seoul National University

Brian Cho, Seoul National University

Byung-Gon Chun, Seoul National University

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BibTeX
@inproceedings {189885,
author = {Joo Seong Jeong and Woo-Yeon Lee and Yunseong Lee and Youngseok Yang and Brian Cho and Byung-Gon Chun},
title = {Elastic Memory: Bring Elasticity Back to {In-Memory} Big Data Analytics},
booktitle = {15th Workshop on Hot Topics in Operating Systems (HotOS XV)},
year = {2015},
address = {Kartause Ittingen, Switzerland},
url = {https://www.usenix.org/conference/hotos15/workshop-program/presentation/jeong},
publisher = {USENIX Association},
month = may,
}
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