ALMEIDA, M. M. S. C.; http://lattes.cnpq.br/6098150643100265; ALMEIDA, Marcella Medeiros Siqueira Coutinho de.
Resumen:
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.