What would you think of a Bugatti Veyron limited to a maximum speed of 20 Km/h?? mmmm… YES!! Give me back the money!!
… well, that’s roughly my feeling when I read a paper on P2P computing restricting the scalability analysis to some few nodes. Of course, the analogy is not completely fair since performing a real massively distributed (and decentralized) experiment presents some challenges that, in most of the cases so far, remain out of the scope of the state of the art research. That happens, for instance, to P2P environments applied to optimization, and more precisely to Evolutionary Computation.
Usually, you can find two different approches for P2P optimization testing, either using simulations or using a “GRID-like style” in real environments, each case presents its own advantanges and drawbacks:
- Using simulations implies a certain number of assumptions about the real environment, hence, assumption have to be correct in some sense (e.g. representing a pesimistic scenario or making clear what’s the scope of the work) and results have to be understood under such an umbrella.
- Using a real P2P environment ( we performed a study like that using a 8×2 cluster). The adventage here is that the design has to face real restrictions, as communication or evaluation times, message passing restrictions or fault tolerance. Nevertheless, there are extreme difficulties to set a real and proper environment to test a model.
When you face a real environment, you find that:
- Large number of resources are hard to grab
- Scalability is hard to study. In the case of having few nodes, no real P2P computing is going on since no conclusions about large scalability can be drawn. On the other hand, if there are some good dozens of peers, other questions such as fault tolerance arise.
In the last friday paper seminar our team was discussing the following paper that uses the “grid-like style” for testing:
in which the authors propose a hybrid model combining islands with cellular EAs. Every peer holds an island and every island a cellular EA. As previously commented for grid-like testing, the scalability analysis is quite poor (up to 10 peers), additionally, the algorithm yields the best performance in the five peers’ scenario, pointing to a really poor scalability of the model. Nevertheless, the fact is that P-CAGE outperforms canonical EAs making of it a valid solution based on P2P technology, just that, such a solution is not really scalable and therefore, can not be really understood as P2P computing.
To conclude, I do think that simulations can benefit the understanding of complex interactions in P2P EAs at this stage of research, preventing situations as the one shown above, afterwards, there will be time to validate the models in real environments, letting that theory meets practice.