Fuel Cell Generation in Geo-Distributed Cloud Services: A Quantitative Study

Abstract

The demand for capping carbon emission has promoted the use of fuel cell energy in cloud computing, yet it is unclear what and how much benefit it may bring. This paper, for the first time, attempts to quantitatively examine the benefits brought by fuel cell generation, and to illustrate how such benefits can be realized with an intelligent coordination between grid power and fuel cell generation. Specifically, we propose UFC, a quantitative index called the utility of the cloud using fuel cells, which captures the level of the data enters operator's overall satisfaction from energy cost, carbon emission, and workload performance. We formulate the UFC maximization problem to jointly optimize both fuel cell generation and geographical request routing. In order to avoid centralized solutions with high complexity and low scalability, we develop a distributed algorithm blending the advantages of Alternating Direction Method of Multipliers (ADMM) and the auxiliary variable method, whose performance is evaluated and verified through our extensive simulations based on real-world data enter workload traces, electricity prices and generation data sets.