NDLI: Evaluating Adaptive Compression to Mitigate the Effects of Shared I/O in Clouds

Content Provider	IEEE Xplore Digital Library
Author	Hovestadt, M. Kao, O. Kliem, A. Warneke, D.
Copyright Year	2011
Abstract	IaaS clouds have become a promising platform for scalable distributed systems in recent years. However, while the virtualization techniques of such clouds are key to the cloud's elasticity, they also result in a reduced and less predictable I/O performance compared to traditional HPC setups. Besides the regular performance degradation of virtualized I/O itself, it is also the potential loss of I/O bandwidth through co-located virtual machines that imposes considerable obstacles for porting data-intensive applications to that platform. In this paper we examine adaptive compression schemes as a means to mitigate the negative effects of shared I/O in IaaS clouds. We discuss the decision models of existing schemes and analyze their applicability in virtualized environments. Based on an evaluation using XEN, KVM, and Amazon EC2, we found that most decision metrics (like CPU utilization and I/O bandwidth) are displayed inaccurately inside virtual machines and can lead to unreasonable levels of compression. As a remedy, we present a new adaptive compression scheme for virtualized environments which solely considers the application data rate. Without requiring any calibration or training phase our adaptive compression scheme can improve the I/O throughput of virtual machines significantly as shown through experimental evaluation.
Starting Page	1042
Ending Page	1051
File Size	722134
Page Count	10
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.256
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Adaptation models Accuracy Bandwidth Throughput Virtual machining Data models
Content Type	Text
Resource Type	Article

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