论文标题
主动自动缩放的案例研究，用于电子商务工作量
A case study of proactive auto-scaling for an ecommerce workload
论文作者
论文摘要
从坎皮纳格兰德大学与电子商务公司之间的合作伙伴关系获得的初步数据表明，某些应用程序在处理可变需求时存在问题。之所以发生这种情况，是因为扩展资源的延迟会导致性能下降，而在文献中，通常是通过改善自动缩放来治疗的问题。为了更好地理解该主题的当前最新技术，我们使用长期的实际工作量重新评估了文献中提出的一种自动缩放算法。实验结果表明，我们的主动方法能够达到高达94％的准确性，并使自动缩放的性能比电子商务公司当前使用的反应性方法更好。
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.
