FAWN: A Fast Array of Wimpy Nodes
Contact: Dave Andersen
"fast, scalable, power-efficient
data-intensive computing"
FAWN is a fast, scalable, and power-efficient cluster architecture for data-intensive computing. Our prototype FAWN cluster 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 1300 queries per second per node, while consuming fewer than 4 watts of power per node.
We have designed and implemented a clustered key-value storage system, FAWN-KV, that runs atop these node. Nodes in FAWN-KV use a specialized log-like back-end hash-based datastore (FAWN-DS) to ensure that the system can absorb the large write workload imposed by frequent node arrivals and departures. FAWN-KV 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.
FAWN 3G, 2G, and 1G Prototypes
People
FACULTY
GRAD STUDENTS
Jason Franklin
Amar Phanishayee
Iulian Moraru
Lawrence Tan
Vijay Vasudevan
Publications
- FAWN: A Fast Array of Wimpy Nodes.
David Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan. Proc. 22nd ACM Symposium on Operating Systems Principles (SOSP 2009), Big Sky, MT. October 2009. BEST PAPER AWARD!
Abstract / PDF [332K]
- FAWNdamentally Power-efficient Clusters.
Vijay Vasudevan, Jason Franklin, David Andersen, Amar Phanishayee, Lawrence Tan, Michael Kaminsky, and Iulian Moraru.
Proc. 12th Workshop on Hot Topics in Operating Systems (HotOS XII), Monte Verita, May 2009.
Abstract / PDF
Acknowledgements
We thank the members and companies of the PDL Consortium: American Power Conversion, Data Domain, Inc., EMC Corporation, Facebook, Google, Hewlett-Packard Labs, Hitachi, IBM, Intel Corporation, LSI, Microsoft Research, NetApp, Inc., Oracle Corporation, Seagate Technology, Sun Microsystems, Symantec Corporation and VMware, Inc. for their interest, insights, feedback, and support.