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freeCycles - Efficient Multi-Cloud Computing Platform
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DOI：10.1007/s10723-017-9414-2.png)
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Abstract
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The growing adoption of the MapReduce programming model increases the appeal of using Internet-wide computing platforms to run MapReduce applications on the Internet. However, current data distribution techniques, used in such platforms to distribute the high volumes of information which are needed to run MapReduce jobs, are naive, and therefore fail to offer an efficient approach for running MapReduce over the Internet. Thus, we propose a computing platform called freeCycles that runs MapReduce jobs over the Internet and provides two new main contributions: i) it improves data distribution, and ii) it increases intermediate data availability by replicating tasks or data through nodes in order to avoid losing intermediate data and consequently avoiding significant delays on the overall MapReduce execution time. We present the design and implementation of freeCycles, in which we use the BitTorrent protocol to distribute all data, along with an extensive set of performance results, which confirm the usefulness of the above mentioned contributions. Our system's improved data distribution and availability makes it an ideal platform for large scale MapReduce jobs.
Keywords:
Cloud computing
BitTorrent
BOINC
MapReduce
Volunteer computing
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