NDLI: Buddies : Exploiting Page Sharing for Server Consolidation in Virtualized Data Centers

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Buddies : Exploiting Page Sharing for Server Consolidation in Virtualized Data Centers

Content Provider	Semantic Scholar
Author	Wood, Timothy Tarasuk-Levin, Gabriel Cipar, Jim Desnoyers, Peter Shenoy, Prashant J. Corner, Mark D.
Copyright Year	2008
Abstract	Modern data centers increasingly employ virtualization in order to maximize resource utilization while reducing costs. With the advent of multi-core processors, memory has become the key limiting resource when running multiple virtual machines on a given server. In this paper, we argue for exploiting page sharing to significantly reduce aggregate memory requirements of collocated VMs and increase the number of VMs that can be housed on a given data center. We present a memory tracer and a novel memory fingerprinting technique—based on Bloom Filters— to concisely and efficiently capture the memory contents of each VM. Our server consolidation algorithm employs a fast, scalable fingerprint comparison technique to efficiently determine which data center servers offer the maximum sharing potential for a given VM, and consolidates these VMs onto these servers via live migration. We also present a hotspot mitigation technique to correct the negative impacts of unanticipated loss of page sharing and swapping. We have implemented a prototype of our Memory Buddies system in the VMware ESX server. An experimental evaluation of our prototype using a mix of enterprise and ecommerce applications demonstrates that exploiting page sharing enables our system to increase the effective capacity of a data center by over 30%, while imposing a CPU and network overhead of less than 0.5% per server and scaling to data centers with up to a thousand servers.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ccs.neu.edu/home/pjd/papers/tr36-07.pdf
Alternate Webpage(s)	http://lass.cs.umass.edu/~pjd/papers/tr36-07.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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