Machine Learning
Linux Journal
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Artificial Neural Networks
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
An Overview of the Granules Runtime for Cloud Computing
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
Performance Measuring and Comparing of Virtual Machine Monitors
EUC '08 Proceedings of the 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing - Volume 02
XenSocket: a high-throughput interdomain transport for virtual machines
Proceedings of the ACM/IFIP/USENIX 2007 International Conference on Middleware
Virtualization polling engine (VPE): using dedicated CPU cores to accelerate I/O virtualization
Proceedings of the 23rd international conference on Supercomputing
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Hadoop: The Definitive Guide
Analyzing Electroencephalograms Using Cloud Computing Techniques
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
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Clouds have become ubiquitous and several data processing tasks have migrated to these settings. The dominant approach in cloud settings is to provision virtual machines (VMs) rather than provision direct access to the physical machine. One artifact of such provisioning is that multiple VMs may be collocated on the same physical machine and possibly interfere with each other. In this paper, we focus on the impact of virtualized infrastructures on real time stream processing, we use the classification of electrocardiograms (ECG) as a motivating example. Stream processing in such a setting strains resources differently than the traditional web services or analytics on large datasets traditionally performed in the cloud. In streaming environments all processing per packet needs to be completed in a timely manner, and the number and rate at which these packets are generated is high. Our focus is to study the implications of various combinations of virtualization strategies on the performance of real time stream processing. We have done extensive performance benchmarks (using Xen and KVM) the results of which form the basis for our recommendations for the trade-offs involved in these settings.