  The rapid development of renewable energy in the energy Internet is expected
to alleviate the increasingly severe power problem in data centers, such as the
huge power costs and pollution. This paper focuses on the eco-friendly power
cost minimization for geo-distributed data centers supplied by multi-source
power, where the geographical scheduling of workload and temporal scheduling of
batteries' charging and discharging are both considered. Especially, we
innovatively propose the Pollution Index Function to model the pollution of
different kinds of power, which can encourage the use of cleaner power and
improve power savings. We first formulate the eco-friendly power cost
minimization problem as a multi-objective and mixed-integer programming
problem, and then simplify it as a single-objective problem with integer
constraints. Secondly, we propose a Sequential Convex Programming (SCP)
algorithm to find the globally optimal non-integer solution of the simplified
problem, which is non-convex, and then propose a low-complexity searching
method to seek for the quasi-optimal mixed-integer solution of it. Finally,
simulation results reveal that our method can improve the clean energy usage up
to 50\%--60\% and achieve power cost savings up to 10\%--30\%, as well as
reduce the delay of requests.
