The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were proposed to this end, including classical resource allocation heuristics, machine learning or stochastic optimization. No consensus exists but a trend towards using many-objective stochastic models became apparent over the past years. This work reviews in brief some of the more recent studies on cloud computing modeling and optimization, and points at notions on stability, convergence, definitions or results that could serve to analyze, respectively build accurate cloud computing models. A very brief discussion of simulation frameworks that include support for energy-aware components is also given.