SCHEDULE: NOV 10-16, 2012
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OpenFlow Enabled Hadoop over Local and Wide Area Clusters
SESSION: SCinet Research Sandbox Presentations continued
EVENT TYPE: SCinet Research Sandboxes
TIME: 10:50AM - 11:10AM
SESSION CHAIR: Andrew Lee
Presenter(s):Stuart Bailey, Anand Daga, Matthew Greenway, Robert Grossman, Sandhya Narayan
ROOM:155-F2
ABSTRACT:
In the recent years, data intensive programming using Hadoop and MapReduce has become increasing important. As normally deployed, Hadoops implementation of MapReduce in a multi-rack cluster is dependent upon the top of the rack switches and of the aggregator switches connecting multiple racks.
We are currently developing Hadoop-OpenFlow Enabled or Hadoop-OFE. The basic idea is to combine OpenFlow (OF) enabled switches and a modified Hadoop JobTracker that is OpenFlow aware in order to improve the performance of Hadoop. Over time our hope is that Hadoop-OFE over standard Ethernet can provide superior performance to Hadoop over specialized interconnects, such as InfiniBand.
We quantify the performance improvement of a Hadoop application called Matsu that is used for processing satellite image data when using Hadoop-OFE versus standard Hadoop. In our Open Cloud Consortium (OCC) SC 12 Research Booth, we will also quantify the performance of Hadoop-OFE running on a wide area network between an OCC Cluster at the University of Chicago and an OCC Cluster at the AMPATH facility in Miami.
Chair/Presenter Details:
Andrew Lee (Chair) - Indiana University
Stuart Bailey - Infoblox
Anand Daga - University of Houston
Matthew Greenway - University of Chicago
Robert Grossman - University of Chicago
Sandhya Narayan - Infoblox
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OpenFlow Enabled Hadoop over Local and Wide Area Clusters
SESSION: SCinet Research Sandbox Presentations continued
EVENT TYPE:
TIME: 10:50AM - 11:10AM
SESSION CHAIR: Andrew Lee
Presenter(s):Stuart Bailey, Anand Daga, Matthew Greenway, Robert Grossman, Sandhya Narayan
ROOM:155-F2
ABSTRACT:
In the recent years, data intensive programming using Hadoop and MapReduce has become increasing important. As normally deployed, Hadoops implementation of MapReduce in a multi-rack cluster is dependent upon the top of the rack switches and of the aggregator switches connecting multiple racks.
We are currently developing Hadoop-OpenFlow Enabled or Hadoop-OFE. The basic idea is to combine OpenFlow (OF) enabled switches and a modified Hadoop JobTracker that is OpenFlow aware in order to improve the performance of Hadoop. Over time our hope is that Hadoop-OFE over standard Ethernet can provide superior performance to Hadoop over specialized interconnects, such as InfiniBand.
We quantify the performance improvement of a Hadoop application called Matsu that is used for processing satellite image data when using Hadoop-OFE versus standard Hadoop. In our Open Cloud Consortium (OCC) SC 12 Research Booth, we will also quantify the performance of Hadoop-OFE running on a wide area network between an OCC Cluster at the University of Chicago and an OCC Cluster at the AMPATH facility in Miami.
Chair/Presenter Details:
Andrew Lee (Chair) - Indiana University
Stuart Bailey - Infoblox
Anand Daga - University of Houston
Matthew Greenway - University of Chicago
Robert Grossman - University of Chicago
Sandhya Narayan - Infoblox
Click here to download .ics calendar file