A Theory of Average-Case Compilability in Knowledge Representation

Resource allocation in computing clusters is traditionally centralized, which limits the cluster scale. Effective resource allocation in a network of computing clusters may enable building larger computing infrastructures. We consider this problem as a novel application for multiagent learning (MAL). We propose a MAL algorithm and apply it for optimizing online resource allocation in cluster networks. The learning is distributed to each cluster, using local information only and without access to the global system reward. Experimental results are encouraging: our multiagent learning approach performs reasonably well, compared to an optimal solution, and better than a centralized myopic allocation approach in some cases.