With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing applications do not utilize heterogeneous resources of edge systems efficiently, leaving scope for performance improvement. In this work, we propose a Proactive Pod Autoscaler (PPA) for edge computing applications on Kubernetes. The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications up and down correspondingly. The PPA is optimized and evaluated on an example CPU-intensive edge computing application further. It can be concluded that the proposed PPA outperforms the default pod autoscaler of Kubernetes on both efficiency of resource utilization and application performance. The article also highlights future possible improvements on the proposed PPA.
翻译:随着Things和5G技术互联网的出现,边际计算模式正在发挥越来越重要的作用,提高了可用性、延缓控制和性能,但是,边际计算应用程序的现有自动扩缩工具没有有效地利用边际系统的各种资源,留下改进业绩的余地。在这项工作中,我们提议为Kubernetes的边际计算应用程序建立一个主动的Pod自动标尺(PPA),拟议的PPPA能够预先预测多种用户定义/定制的计量标准的工作量,并相应地将边际计算应用程序向上和向下推缩。PPPA被优化,在CPU密集的边际计算应用程序中进一步评估。可以得出结论,拟议的PPPA在资源利用效率和应用业绩方面比Kubernetes的默认自动标尺器要强。文章还重点介绍了拟议的PPA今后可能作出的改进。