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.
@article{arxiv.2211.11928,
title = {A case study of proactive auto-scaling for an ecommerce workload},
author = {Marcella Medeiros Siqueira Coutinho de Almeida and Thiago Emmanuel Pereira and Fabio Morais},
journal= {arXiv preprint arXiv:2211.11928},
year = {2022}
}