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FAWN: A Fast Array of Wimpy Nodes David G. Andersen, Jason Franklin, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan
 

Summary: FAWN: A Fast Array of Wimpy Nodes
David G. Andersen, Jason Franklin, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan
Carnegie Mellon University
CMU-PDL-08-108
May 2008
Parallel Data Laboratory
Carnegie Mellon University
Pittsburgh, PA 15213-3890
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 (216GB) 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)

  

Source: Andersen, Dave - School of Computer Science, Carnegie Mellon University
Carnegie Mellon University, Department of Electrical and Computer Engineering, Parallel Data Lab.

 

Collections: Computer Technologies and Information Sciences