I am designing a system that includes a neural network engine. An equivalent
system is currently working on the MS platform, but is very unscalable.
I want to be able to pool neural network beans, as requests for searches are
fairly rare, but come from many many clients.
However, there are many different instances of NN, all represented by the
same class.
Specifically, there is a NN for each category that the user can perform a
search against. These categories are completely unrelated to each other.
The stae of each net is stored in a file. For a large net, it can take in
the order of 2 seconds to read the file and build the net, using highly
optimised C++, so I can't load the net on each request (search turnaround
must be sub one second).
I have determined (so far) the following possibilities:
1. Maintain a pool of session beans, each of which loads all nets. However,
each session bean could become quite large. Is there any way for me to
determine how large? I know how to work out how many nodes and arcs a net
will have from a given data set, and how many ints, floats ...etc that will
be created, but I don't think that just adding up the storage requirements
of the native types at the leaves of the object tree will tell me how much
storage will actually be need by an instance of a bean running in a
container.
2. Represent each net as an entity bean and then have session beans that do
findByPrimaryKey to get a handle on the correct net during the session. What
happens if two sessions both try to call the same method on it at the same
time? And how do I cope with the fact that the entity does not actually
represent a db entity?
3. A third aletrnative is to have session beans that use an LRU algorithm to
maintain a truncated list of nets. This would mean that the user would
occaisionally experience slight delay as the appropriate net is loaded.
(Load time could be reduced to the order of .5 seconds in this scenario, by
using a selective load algorithm).
It seems like in point 3 I am trying to do something that EJB itself should
be doing, and I wonder if I'm missing something.
If anyone can comment on this, I'd be very grateful.
Phil Lewis
Technical Architect
Neural Response Ltd
[EMAIL PROTECTED]
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