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.
翻译：从Campina Grande联邦大学与一家电子商务公司之间的伙伴关系中获得的初步数据表明,在处理可变需求时,有些应用软件存在问题,这是因为资源规模的延迟导致性能退化,在文献中,通常通过改进自动缩放处理。为了更好地了解目前关于这一问题的最新技术,我们利用长期实际工作量,重新评估了文献中提议的电子商务自动缩放算法。实验结果表明,我们积极主动的做法能够达到94%的准确率,并导致自动缩放比电子商务公司目前采用的被动反应方法业绩更好。
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