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SnowFlock: rapid virtual machine cloning for cloud computing

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European Conference on Computer Systems archive
Proceedings of the 4th ACM European conference on Computer systems table of contents

Nuremberg, Germany

SESSION: Cloud computing table of contents

Pages 1-12

Year of Publication: 2009

ISBN:978-1-60558-482-9

Authors

Horacio Andrés Lagar-Cavilla	University of Toronto, Toronto, ON, Canada
Joseph Andrew Whitney	University of Toronto, Toronto, ON, Canada
Adin Matthew Scannell	University of Toronto, Toronto, ON, Canada
Philip Patchin	University of Toronto, Toronto, ON, Canada
Stephen M. Rumble	University of Toronto, Toronto, ON, Canada
Eyal de Lara	University of Toronto, Toronto, ON, Canada
Michael Brudno	University of Toronto, Toronto, ON, Canada
Mahadev Satyanarayanan	Carnegie Mellon University, Pittsburgh, PA, USA

Sponsor

ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 159, Downloads (12 Months): 681, Citation Count: 1

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ABSTRACT

Virtual Machine (VM) fork is a new cloud computing abstraction that instantaneously clones a VM into multiple replicas running on different hosts. All replicas share the same initial state, matching the intuitive semantics of stateful worker creation. VM fork thus enables the straightforward creation and efficient deployment of many tasks demanding swift instantiation of stateful workers in a cloud environment, e.g. excess load handling, opportunistic job placement, or parallel computing. Lack of instantaneous stateful cloning forces users of cloud computing into ad hoc practices to manage application state and cycle provisioning. We present SnowFlock, our implementation of the VM fork abstraction. To evaluate SnowFlock, we focus on the demanding scenario of services requiring on-the-fly creation of hundreds of parallel workers in order to solve computationally-intensive queries in seconds. These services are prominent in fields such as bioinformatics, finance, and rendering. SnowFlock provides sub-second VM cloning, scales to hundreds of workers, consumes few cloud I/O resources, and has negligible runtime overhead.

REFERENCES

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