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arXiv · 2211.11928

A case study of proactive auto-scaling for an ecommerce workload

Marcella Medeiros Siqueira Coutinho de Almeida·Thiago Emmanuel Pereira·Fabio Morais
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Abstract

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.

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Marcella Medeiros Siqueira Coutinho de Almeida, Thiago Emmanuel Pereira, Fabio Morais. 2022-11-22. A case study of proactive auto-scaling for an ecommerce workload. https://arxiv.org/abs/2211.11928

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