Hadoop extensions for distributed computing on reconfigurable active SSD clusters
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Association for Computing Machinery
Abstract
In this article, we propose new extensions to Hadoop to enable clusters of reconfigurable active solid-state drives (RASSDs) to process streaming data from SSDs using FPGAs. We also develop an analytical model to estimate the performance of RASSD clusters running under Hadoop. Using the Hadoop RASSD platform and network simulators, we validate our design and demonstrate its impact on performance for different workloads taken from Stanford's Phoenix MapReduce project. Our results show that for a hardware acceleration factor of 20×, compute-intensive workloads processing 153MB of data can run up to 11× faster than a standard Hadoop cluster. © 2014 ACM.
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Keywords
Active storage, Data-intensive computing, Middleware, Digital storage, Field programmable gate arrays (fpga), Hardware acceleration, Map-reduce, Network simulators, Reconfigurable, Solid-state drives, Streaming data, Reconfigurable hardware