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From "Mattmann, Chris A (388J)" <chris.a.mattm...@jpl.nasa.gov>
Subject Re: Similar frameworks like hadoop and taxonomy of distributed computing
Date Thu, 12 Jan 2012 02:54:45 GMT
Here's some links to it:

Long Version: http://csse.usc.edu/csse/TECHRPTS/2008/usc-csse-2008-820/usc-csse-2008-820.pdf
Shorter Version (published in WICSA): http://wwwp.dnsalias.org/w/images/3/3f/AnatomyPhysiologyGridRevisited66.pdf


On Jan 11, 2012, at 4:02 PM, Mattmann, Chris A (388J) wrote:

> Also check out my paper on The Anatomy and Physiology of the Grid Revisited just Google
for it where we also tried to look at this very issue.
> Cheers,
> Chris 
> Sent from my iPhone
> On Jan 11, 2012, at 3:55 PM, "Brian Bockelman" <bbockelm@cse.unl.edu> wrote:
>> On Jan 11, 2012, at 10:15 AM, George Kousiouris wrote:
>>> Hi,
>>> see comments in text
>>> On 1/11/2012 4:42 PM, Merto Mertek wrote:
>>>> Hi,
>>>> I was wondering if anyone knows any paper discussing and comparing the
>>>> mentioned topic. I am a little bit confused about the classification of
>>>> hadoop.. Is it a /cluster/comp grid/ a mix of them?
>>> I think that a strict definition would be an implementation of the map-reduce
computing paradigm, for cluster usage.
>>>> What is hadoop in
>>>> relation with a cloud - probably just a technology that enables cloud
>>>> services..
>>> It can be used to enable cloud services through a service oriented framework,
like we are doing in
>>> http://users.ntua.gr/gkousiou/publications/PID2095917.pdf
>>> in which we are trying to create a cloud service that offers MapReduce clusters
as a service and distributed storage (through HDFS).
>>> But this is not the primary usage. This is the back end heavy processing in a
cluster-like manner, specifically for parallel jobs that follow the MR logic.
>>>> Can it be compared to cluster middleware like beowulf, oscar, condor,
>>>> sector/sphere, hpcc, dryad, etc? Why not?
>>> I could see some similarities with condor, mainly in the job submission processes,
however i am not really sure how condor deals with parallel jobs.
>> Since you asked…
>> <condor-geek>
>> Condor has a built-in concept of a set of jobs (called a "job cluster").  On top
of its scheduler, there is a product called "DAGMan" (DAG = directed acyclic graph) that can
manage a large number of jobs with interrelated dependencies (providing a partial ordering
between jobs).  Condor with DAG is somewhat comparable to the concept of Hadoop tasks plus
Oozie workflows (although the data aspects are very different - don't try to stretch it too
>> Condor / PBS / LSF / {OGE,SGE,GE} / SLURM provide the capability to start many identical
jobs in parallel for MPI-type computations, but I consider MPI wildly different than the sort
of workflows you see with MapReduce.  Specifically, "classic MPI"  programming (the ones you
see in wide use, MPI2 and later are improved) mostly requires all processes to start simultaneously
and the job crashes if one process dies.  I think this is why the Top10 computers tend to
measure mean time between failure in tens of hours.
>> Unlike Hadoop, Condor jobs can flow between pools (they call this "flocking") and
pools can naturally cover multiple data centers.  The largest demonstration I'm aware of is
100,000 cores across the US; the largest production pool I'm aware of is about 20-30k cores
across 100 universities/labs on multiple continents.  This is not a criticism of Hadoop -
Condor doesn't really have the same level of data-integration as Hadoop does, so tackles a
much simpler problem (i.e., bring-your-own-data-management!).
>> </condor-geek>
>> Brian

Chris Mattmann, Ph.D.
Senior Computer Scientist
NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
Office: 171-266B, Mailstop: 171-246
Email: chris.a.mattmann@nasa.gov
WWW:   http://sunset.usc.edu/~mattmann/
Adjunct Assistant Professor, Computer Science Department
University of Southern California, Los Angeles, CA 90089 USA

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