Autoscaling Method for Docker Swarm Towards Bursty Workload - Huang, Qichen; Wang, Song; Ding, Zhijun (Computing and Informatics, 2024)
The autoscaling mechanism of cloud computing can automatically adjust computing resources according to user needs, improve quality of service (QoS) and avoid over-provision. However, the traditional autoscaling methods suffer from oscillation and degradation of QoS when dealing with burstiness. Therefore, the autoscaling algorithm should consider the effect of bursty workloads. In this paper, we propose a novel AmRP (an autoscaling method that combines reactive and proactive mechanisms) that uses proactive scaling to launch some containers in advance, and then the reactive module performs vertical scaling based on existing containers to increase resources rapidly. Our method also integrates burst detection to alleviate the oscillation of the scaling algorithm and improve the QoS. Finally, we evaluated our approach with state-of-the-art baseline scaling methods under different workloads in a Docker Swarm cluster. Compared with the baseline methods, the experimental results show that AmRP has fewer SLA violations when dealing with bursty workloads, and its resource cost is also lower.