  Preliminary data obtained from a partnership between the Federal University
of Campina Grande and an ecommerce company indicates that some applications
have issues when dealing with variable demand. This happens because a delay in
scaling resources leads to performance degradation and, in literature, is a
matter usually treated by improving the auto-scaling. To better understand the
current state-of-the-art on this subject, we re-evaluate an auto-scaling
algorithm proposed in the literature, in the context of ecommerce, using a
long-term real workload. Experimental results show that our proactive approach
is able to achieve an accuracy of up to 94 percent and led the auto-scaling to
a better performance than the reactive approach currently used by the ecommerce
company.
