Dynamic on Demand Virtual Clusters in Grid
In Grid environments, many different resources are intended to work in a coordinated manner, each resource having its own features and complexity. As the number of resources grows, simplifying automation and management is among the most important issues to address. This paper’s contribution lies on the extension and implementation of a grid metascheduler that dynamically discovers, creates and manages on-demand virtual clusters. The first module selects the clusters using graph heuristics. The algorithm then tries to find a solution by searching a set of clusters, mapped to the graph, that achieve the best performance for a given task. The second module, one per-grid node, monitors and manages physical and virtual machines. When a new task arrives, these modules modify virtual machine’s configuration or use live migration to dynamically adapt resource distribution at the clusters, obtaining maximum utilization. Metascheduler components and local administrator modules work together to make decisions at run time to balance and optimize system throughput. This implementation results in performance improvement of 20% on the total computing time, with machines and clusters processing 100% of their working time. These results allow us to conclude that this solution is feasible to be implemented on Grid environments, where automation and self-management are key to attain effective resource usage.
KeywordsExecution Time Virtual Machine Minimum Span Tree Grid Environment Virtual Machine Migration
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- 1.Grosclaude, E., Luro, F.L., Bertogna, M.L.: Grid Virtual Laboratory Architecture. In: VHPC Euro-Par 2007 (2007)Google Scholar
- 2.Open source metascheduling for Virtual Organizations with the Community Scheduler Framework (2004), http://www.cs.virginia.edu/~grimshaw/CS851-2004/Platform/CSF_architecture.pdf
- 4.Foster, I., Freeman, T., Keahey, K., Scheftner, D., Sotomayor, B., Zhang, X.: Virtual Clusters for Grid Communities. In: CCGrid 2006 (2006)Google Scholar
- 5.Barham, P.T., Dragovic, B., Fraser, K., Hand, S., Harris, T.L., Neugebauer, R.: Xen and the Art of Virtualization. In: SOSP 2003, pp. 164–177 (2003)Google Scholar
- 6.Clark, C., Fraser, K., Hand, S., Hansen, J.G.: Live Migration of Virtual Machines. In: Proceedings of the 2nd ACM/USENIX Symposium on Networked Systems Design and Implementation (2005)Google Scholar
- 7.Dong, F., Akl, S.G.: Scheduling Algorithms for Grid Computing: State of the Art and Open Problems,Technical Report No. 2006-504, Queen’s University, Canada, 55 pages (2006)Google Scholar
- 8.Zhu, Y.: A survey on grid scheduling systems, Technical Report, Computer Science Department of Hong Kong University of Science and Technology (2003)Google Scholar
- 9.Ruth, P., McGachey, P., Xu, D.: VioCluster: Virtualization for Dynamic Computational Domains. In: Proceedings of the IEEE International Conference on Cluster Computing, Cluster 2005 (2005)Google Scholar