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

TL;DR

This paper evaluates a proactive auto-scaling algorithm for ecommerce workloads, demonstrating improved accuracy and performance over reactive methods through long-term real workload experiments.

Contribution

It re-evaluates an existing auto-scaling algorithm in ecommerce context using real workload data, showing its effectiveness and advantages.

Findings

Proactive auto-scaling achieved up to 94% accuracy.

The proactive approach outperformed reactive auto-scaling.

Long-term experiments validated the method's effectiveness.

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.