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<div class="moz-cite-prefix">On 06-02-14 14:17, Mats Norén wrote:<br>
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<div dir="ltr">Hi,
<div>I have a use case where multiple users need to run multiple
simultaneous solvers.</div>
<div>Is there a best practice in scaling out Optaplanner for
multitenancy?</div>
<div>Just to be clear I'm not interested in scaling out a single
optimization run but instead multiple solvers.</div>
<div>So I'm looking for pros and cons for:</div>
<div>- multiple solvers in the same JVM</div>
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Provide 1 CPU core per Solver that's running in parallel.<br>
1 Solver saturates 1 CPU core entirely<br>
and it is ussually happy if it has 300mb of free heap space on top
of it needs as a bare minimum.<br>
If there is no such free heap space, you're basically trading more
cpu for less memory (due to higher GC activity).<br>
<br>
So provide 1 CPU core per Solver that's running in parallel.<br>
If you need to run 8 jobs and have 4 CPU cores, I would probably run
4 at the same time for half the time.<br>
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<div>- multiple JVM:s on a single machine (with multiple
solvers)</div>
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I see no reason to do that. By using 1 JVM you pool class instances,
etc so use less memory overall.<br>
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<div>- multiple machine setup</div>
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Definitely the way to increase Solvers running in parallel.<br>
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<div>- virtual machine vs lxc (Docker) vs bare metal</div>
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No data on that yet.<br>
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<div><br>
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<div>I'm also interested in provisioning of rules etc <br>
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Let me know what works well for you and what doesn't with
multitenancy!<br>
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<div><br>
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<div>Regards,</div>
<div>Mats</div>
<div><br>
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