We can use Size of data on which job is dependent to determine if it is data incentive, and instead of having a fixed Size to determine we can make it adapt based on the situations like.. in case rate of jobs being submitted is increasing but so is the idle resource pool then the Size limit has to be raised so that jobs are assigned to new resources and if in case rate of job submission is going down but idle resource pool is decreasing or remains constant the Size can be increased... or we can monitor the data transfers if they are above some threshold Size can be decreased and vice-versa ... <br>
<br><div class="gmail_quote">On Sat, Mar 20, 2010 at 2:16 AM, Ioan Raicu <span dir="ltr"><<a href="mailto:iraicu@cs.uchicago.edu">iraicu@cs.uchicago.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div bgcolor="#ffffff" text="#000000">
For data intensive workloads, it usually is. The question is how do you
determine "overloaded", and "data-intensive"? <br>
<br>
Ioan<br>
<br>
Mihael Hategan wrote:
<blockquote type="cite"><div><div></div><div class="h5">
<pre>On Sat, 2010-03-20 at 01:57 +0530, Vipul Kumar Singh wrote:
</pre>
<blockquote type="cite">
<pre> (iii) When scheduling new jobs, the scheduler looks
through the hash and schedules new job to the resource that has data
on which new job is dependent (If the resource is not already
overloaded).
</pre>
</blockquote>
</div></div><pre>Is that always the best choice?
Mihael
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</blockquote>
<br>
<pre cols="72">--
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Ioan Raicu, Ph.D.
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</pre>
</div>
</blockquote></div><br>