  "Geographic Load Balancing" is a strategy for reducing the energy cost of
data centers spreading across different terrestrial locations. In this paper,
we focus on load balancing among micro-datacenters powered by renewable energy
sources. We model via a Markov Chain the problem of scheduling jobs by
prioritizing datacenters where renewable energy is currently available. Not
finding a convenient closed form solution for the resulting chain, we use mean
field techniques to derive an asymptotic approximate model which instead is
shown to have an extremely simple and intuitive steady state solution. After
proving, using both theoretical and discrete event simulation results, that the
system performance converges to the asymptotic model for an increasing number
of datacenters, we exploit the simple closed form model's solution to
investigate relationships and trade-offs among the various system parameters.
