  Unexpected loads in Cloud data centers may trigger overloaded situation and
performance degradation. To guarantee system performance, cloud computing
environment is required to have the ability to handle overloads. The existing
approaches, like Dynamic Voltage Frequency Scaling and VM consolidation, are
effective in handling partial overloads, however, they cannot function when the
whole data center is overloaded. Brownout has been proved to be a promising
approach to relieve the overloads through deactivating application
non-mandatory components or microservices temporarily. Moreover, brownout has
been applied to reduce data center energy consumption. It shows that there are
trade-offs between energy saving and discount offered to users (revenue loss)
when one or more services are not provided temporarily. In this paper, we
propose a brownout-based approximate Markov Decision Process approach to
improve the aforementioned trade-offs. The results based on real trace
demonstrate that our approach saves 20% energy consumption than VM
consolidation approach. Compared with existing energy-efficient brownout
approach, our approach reduces the discount amount given to users while saving
similar energy consumption.
