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

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