2022/11/22 by Marcella Medeiros Siqueira Coutinho de Almeida, de Almeida, Marcella Medeiros Siqueira Coutinho, Thiago Emmanuel Pereira +3
Computer Science · #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2211.11928
openalex publication_date 2022/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.