<p>I'm interested in deploying an optaplanner solution on a Kubernetes cluster. The main interest would be to scale horizontaly the solving of a specific dataset.</p>
<p>I have seen an example of optaplanner with a cluster (implemented thanks to an Optaplanner operator) but in my understanding it scales when multiple problems are submitted (one pod per problem)</p>
<p>So this is not what I'm looking for.</p>
<p>I'm looking for a way to &quot;multithread&quot; the computation of the problem (like with the move-thread-count) but accross multiple pods.</p>
<p>Do you know or have heard of any way to do it? I'm not even sure that Optaplanner supports it...</p>
<p>this <a href="https://stackoverflow.com/questions/71130151/how-can-i-use-multiple-dynos-for-an-optaplanner-process-on-heroku">answer</a> says it's not yet supported natively but speak about doing it manually.</p>
<p>Do you have any tips to give for a start?
I would rather find a way to not do partitionned solving since it can miss the optimal solution..</p>

minioim
<p>That depends.</p>
<p>Are we talking about multi-bet solving? Essentially running the solver in N  clones simultaneously, and picking the best single result out of them? If so, that's where the Operator can help as well.</p>
<p>If we're talking about solving the same problem in a distributed manner, with different machines executing different moves and then incorporating them into one single solution, then no, there is no out-of-the-box solution for that, and I haven't seen any custom implementations of that either.</p>
<p>Honestly, I don't see the benefits of that - the async overhead (and network latency!) will kill performance. The multi-threaded solver already suffers from that, and that's running on a single machine.</p>
<p>My opinion: Having 1 pod run N threads makes just so much more sense to me than having N pods run 1 thread.</p>
