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Efficiently Synchronizing Virtual Machines in Cloud Computing Environments

Authors: Shuntaro Tonosaki; Hiroshi Yamada; Kenji Kono;

Efficiently Synchronizing Virtual Machines in Cloud Computing Environments

Abstract

Infrastructure as a Service (IaaS), a form of cloud computing, is gaining attention for its ability to enable efficient server administration in dynamic workload environments. In such environments, however, updating the software stack or content files of virtual machines (VMs) is a time-consuming task, discouraging administrators from frequently enhancing their services and fixing security holes. This is because the administrator has to upload the whole new disk image to the cloud platform via the Internet, which is not yet fast enough that large amounts of data can be transferred smoothly. Although the administrator can apply only incremental updates directly to the running VMs, he or she has to carefully consider the type of update and perform operations on all the running VMs, such as application restarts and operating system reboots. This is a tedious and error-prone task. This paper presents a technique for synchronizing VMs with less time and lower administrative burden. We introduce the Virtual Disk Image Repository, which runs on the cloud platform and automatically updates the virtual disk image and the running VMs with only the incremental update information. We also show a mechanism that performs necessary operations on the running VM such as restarting server processes, based on the types of files that are updated. We implemented a prototype on Linux 2.6.31.14 and Amazon Elastic Compute Cloud. The experimental results show that our technique can synchronize VMs in an order-of-magnitude shorter time than the conventional disk-image-based VM cloning method. Although our system imposes about 30% overhead on the developer's environment, it imposes no observable overhead on public servers and correctly performs necessary operations to put updates into effect.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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