Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64320
Title: Tetris : optimizing cloud resource usage unbalance with elastic VM
Authors: Ling, X
Yuan, Y
Wang, D 
Yang, JH
Keywords: Virtual machines
Big Data
Cloud computing
Pattern clustering
Resource allocation
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, China, June 20-21, 2016, p. 1-10 How to cite?
Abstract: Recently, the cloud systems face an increasing number of big data applications. It becomes an important issue for the cloud providers to allocate resources so as to accommodate as many of these big data applications as possible. In current cloud service, e.g., Amazon EMR, a job runs on a fixed cluster. This means that a fixed amount of resources (e.g. CPU, memory) is allocated to the life cycle of this job. We observe that the resources are inefficiently used in such services because of resources usage unbalance. Therefore, we propose a runtime elastic VM approach where the cloud system can increase or decrease the number of CPUs at different time periods for the jobs. There is little change to such services as Amazon EMR, yet the cloud system can accommodate many more jobs. In this paper, we first present a measurement study to show the feasibility and the quantitative impact of adjusting VM configurations dynamically. We then model the task and job completion time of big data applications, which are used for elastic VM adjustment decisions. We validate our models through experiments. We present Tetris, an elastic VM strategy based on cloud system that can better optimize resource utilization to support big data applications. We further implement a Tetris prototype and comprehensively evaluate Tetris on a real private cloud platform using Facebook trace and Wikipedia dataset. We observe that with Tetris, the cloud system can accommodate 31.3% more jobs.
URI: http://hdl.handle.net/10397/64320
ISBN: 978-1-5090-2634-0 (electronic)
978-1-5090-2635-7 (Print on Demand(PoD))
DOI: 10.1109/IWQoS.2016.7590395
Appears in Collections:Conference Paper

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