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of green energy when not considering the switching
on/off energy costs. It also exhibits good results in
terms of brown energy consumption with a differ-
ence of 4% with the optimal when not considering
the switching on/off energy costs. When consider-
ing the switching on/off energy costs, SAGITTA con-
sumes 10% more brown energy than the theoretical
lower bound, which is the ideal allocation not taking
into account the switching on/off energy costs. We
study the influence of the green energy production on
SAGITTA’s energy gains and show that, in all cases,
it outperforms traditional approaches. The results
also show that SAGITTA can smoothly scale with the
number of data centers belonging to the cloud.
We plan to extend this work by considering the
impact of network devices on the energy consumption
and integrating the ability to dynamically migrate vir-
tual machines from one site to another.
ACKNOWLEDGEMENTS
The authors would like to thank Yunbo Li for the
energy traces of real datacenter servers. The au-
thors would also like to thank Matthieu Simonin and
Nathalie Bertrand for their proofreading of the math-
ematical proofs. This work has been supported by the
Inria exploratory research project COSMIC (Coordi-
nated Optimization of SMart grIds and Clouds).
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