PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Parallel programming in OpenMP
Parallel programming in OpenMP
Dynamic Virtual Clusters in a Grid Site Manager
HPDC '03 Proceedings of the 12th 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
Scale and performance in the Denali isolation kernel
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Fault Tolerance in Message Passing Interface Programs
International Journal of High Performance Computing Applications
Scalability, fidelity, and containment in the potemkin virtual honeyfarm
Proceedings of the twentieth ACM symposium on Operating systems principles
Virtual Clusters for Grid Communities
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating the Performance Impact of Xen on MPI and Process Execution For HPC Systems
VTDC '06 Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing
Remus: high availability via asynchronous virtual machine replication
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
SnowFlock: rapid virtual machine cloning for cloud computing
Proceedings of the 4th ACM European conference on Computer systems
CLUSTER '07 Proceedings of the 2007 IEEE International Conference on Cluster Computing
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Cloud computing promises to provide researchers with the ability to perform parallel computations using large pools of virtual machines (VMs), without facing the burden of owning or maintaining physical infrastructure. However, with ease of access to hundreds of VMs, comes also an increased management burden. Cloud users today must manually instantiate, configure and maintain the virtual hosts in their cluster. They must learn new cloud APIs that are not germane to the problem of parallel processing. Those APIs usually take several minutes to perform their VM-management tasks, forcing users to keep VMs idling and pay for unused processing time, rather than shut VMs down and power them on as needed. Furthermore, users must still configure their cluster management framework to launch their parallel jobs. In this paper we show that all this management pain is unnecessary. We show how to combine a cloud API -- SnowFlock -- and a parallel processing framework -- MPI -- to truly realize the potential of the cloud. SnowFlock allows users to fork VMs as if they were processes, occupying in sub-second time multiple physical hosts. We exploit the synergy between this paradigm and MPI's job management to completely hide all details of cloud management from the user. Maintaining a single VM and starting unmodified applications with familiar MPI commands, a user can instantaneously leverage hundreds of processors to perform a parallel computation. Besides making use of cloud resources trivial, we also eliminate the cost of idling -- VMs exist only for as long as they are involved in computation.