  Storage systems for cloud computing merge a large number of commodity
computers into a single large storage pool. It provides high-performance
storage over an unreliable, and dynamic network at a lower cost than purchasing
and maintaining large mainframe. In this paper, we examine whether it is
feasible to apply Reinforcement Learning(RL) to system domain problems. Our
experiments show that the RL model is comparable, even outperform other
heuristics for block management problem. However, our experiments are limited
in terms of scalability and fidelity. Even though our formulation is not very
practical,applying Reinforcement Learning to system domain could offer good
alternatives to existing heuristics.
