Entropy is the best predictor of volunteer computing system performance

In volunteer computing systems the users get to decide when, and how much, their own computers are going to be working in a particular problem. We have been working for some time in using volunteer computing for evolutionary algorithms, and all our efforts have focused in having a scalable back end and also finding how the user behaves in order to understand the behavior. A priori, one would think that the more users, the better. However, the fact that these systems are asynchronous and have heterogeneous capabilities means that it might happen that new users do not really have any contribution to the overall effort.
In this paper presented at the EvoStar conference this week, we took a different approach to analyzing performance by using compression entropy, computed over the number of contributions per minute. The bigger compression, the more uniform contributions are; the lower the compression, that means that the contributions change all the time. After some preliminary reports published in FigShare we found that there is a clear trend in an increasing entropy making the algorithm end much faster. This contradicts our initial guess, and also opens new avenues for the design of volunteer evolutionary computing systems, and probably other systems whose performande depends on diversity such as evolutionary algorithms.
Check out the poster and also the presentation done at the conference. You will miss, however, the tulip origami we gave out to the visitors of the poster.
In our research group we support open science, that is why you can find everything, from data to processing scripts to the sources of this paper, in the GitHub repository

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