Maestro-VC: A Paravirtualized Execution Environment for Secure On-Demand Cluster Computing
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Virtual hierarchies to support server consolidation
Proceedings of the 34th annual international symposium on Computer architecture
An Evaluation of Server Consolidation Workloads for Multi-Core Designs
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Using clouds to provide grids with higher levels of abstraction and explicit support for usage modes
Concurrency and Computation: Practice & Experience - A Special Issue from the Open Grid Forum
Virtual Machine Scalability on Multi-Core Processors Based Servers for Cloud Computing Workloads
NAS '09 Proceedings of the 2009 IEEE International Conference on Networking, Architecture, and Storage
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Magellan: experiences from a science cloud
Proceedings of the 2nd international workshop on Scientific cloud computing
Performance and deployment evaluation of a parallel application on a private Cloud
Concurrency and Computation: Practice & Experience
A Representation Model for Virtual Machine Allocation
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Proceedings of the 28th Annual ACM Symposium on Applied Computing
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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This paper explores some of the effects that the paradigm of Cloud Computing has on schedulers when executing scientific applications. We present premises regarding to provisioning and architectural aspects of a Cloud infrastructure, that are not present in other environments, and which implications they may have on scheduling decisions in presence of relevant policies like improving performance. We also argue that using virtualization as a mechanism for workload consolidation in a multi-core environment has important performance consequences for e-science. We propose and test a preliminary workload classification, based on usage modes, that may improve early scheduling decisions as we research towards automatic deployment of scientific applications.