FAWN: A Fast Array of Wimpy Nodes

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FAWN: A Fast Array of Wimpy Nodes (2008)

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by David G. Andersen , Jason Franklin , Amar Phanishayee , Lawrence Tan , Vijay Vasudevan

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212 - 26 self

BibTeX

@MISC{Andersen08fawn:a,
    author = {David G. Andersen and Jason Franklin and Amar Phanishayee and Lawrence Tan and Vijay Vasudevan},
    title = {FAWN: A Fast Array of Wimpy Nodes},
    year = {2008}
}

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Abstract

This paper introduces the FAWN—Fast Array of Wimpy Nodes—cluster architecture for providing fast, scalable, and power-efficient key-value storage. A FAWN links together a large number of tiny nodes built using embedded processors and small amounts (2–16GB) of flash memory into an ensemble capable of handling 700 queries per second per node, while consuming fewer than 6 watts of power per node. We have designed and implemented a clustered key-value storage system, FAWN-DHT, that runs atop these node. Nodes in FAWN-DHT use a specialized log-like back-end hash-based database to ensure that the system can absorb the large write workload imposed by frequent node arrivals and departures. FAWN uses a two-level cache hierarchy to ensure that imbalanced workloads cannot create hot-spots on one or a few wimpy nodes that impair the system’s ability to service queries at its guaranteed rate. Our evaluation of a small-scale FAWN cluster and several candidate FAWN node systems suggest that FAWN can be a practical approach to building large-scale storage for seek-intensive workloads. Our further analysis indicates that a FAWN cluster is cost-competitive with other approaches (e.g., DRAM, multitudes of magnetic disks, solid-state disk) to providing high query rates, while consuming 3-10x less power. Acknowledgements: We thank the members and companies of the CyLab Corporate Partners and the PDL

Keyphrases

wimpy node fast array fawn cluster flash memory wimpy node cluster architecture large number several candidate fawn high query rate tiny node solid-state disk two-level cache hierarchy system ability seek-intensive workload large write workload imbalanced workload cannot create hot-spots cylab corporate partner power-efficient key-value storage large-scale storage clustered key-value storage system embedded processor small-scale fawn cluster service query small amount specialized log-like back-end hash-based database fawn fast array fawn-dht use practical approach magnetic disk guaranteed rate frequent node arrival

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