Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
A taxonomy of Data Grids for distributed data sharing, management, and processing
ACM Computing Surveys (CSUR)
Virtual Clusters for Grid Communities
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Virtual Clusters on the Fly - Fast, Scalable, and Flexible Installation
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
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Federated storage resources in geographically distributed environments are becoming viable platforms for data-intensive cloud and grid applications. To improveI /O performance in such environments, we propose a novel model-based I/O performance optimization algorithm for data-intensive applications running on a virtual cluster, which determines virtual machine (VM) migration strategies,i.e., when and where a VM should be migrated, while minimizing the expected value of file access time. We solve this problem as a shortest path problem of a weighted direct acyclic graph (DAG), where the weighted vertex represents a location of a VM and expected file access time from the location, and the weighted edge represents a migration of a VM and time. We construct the DAG from our markov model which represents the dependency of files. Our simulation-based studies suggest that our proposed algorithm can achieve higher performance than simple techniques, such as ones that never migrate VMs: 38% or always migrate VMs onto the locations that hold target files: 47%.