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Cited by 324

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Sandpiper: Black-box and gray-box resource management for virtual machines

Open access
Wood, Timothy, Shenoy, Prashant, Venkataramani, Arun, Yousif, Mazin·2009·Computer Networks

Abstract

Virtualization can provide significant benefits in data centers by enabling dynamic virtual machine resizing and migration to eliminate hotspots. We present Sandpiper, a system that automates the task of monitoring and detecting hotspots, determining a new mapping of physical to virtual resources, resizing virtual machines to their new allocations, and initiating any necessary migrations. Sandpiper implements a black-box approach that is fully OS- and application-agnostic and a gray-box approach that exploits OS- and application-level statistics. We implement our techniques in Xen and conduct a detailed evaluation using a mix of CPU, network and memory-intensive applications. Our results show that Sandpiper is able to resolve single server hotspots within 20s and scales well to larger, data center environments. We also show that the gray-box approach can help Sandpiper make more informed decisions, particularly in response to memory pressure.

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Crossref · 10.1016/j.comnet.2009.04.014

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Metadata

Authors
Wood, T., Shenoy, P., Venkataramani, A., & Yousif, M.
Venue
Computer Networks
Publisher
Elsevier BV
Published
2009-12
Volume
53
Issue
17
Pages
2923-2938
ISSN
1389-1286 (ISSN-L)
OpenAlex
W1988497614
Cited by
324
References
19
FWCI
41.49
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Sandpiper: Black-box and gray-box resource management for virtual machines. https://app.cochat.ai/papers/10.1016/j.comnet.2009.04.014
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Wood, T., Shenoy, P., Venkataramani, A., & Yousif, M. (2009). Sandpiper: Black-box and gray-box resource management for virtual machines. Computer Networks. https://doi.org/10.1016/j.comnet.2009.04.014

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.