云计算
计算机科学
可扩展性
工作量
分布式计算
资源配置
资源(消歧)
容器(类型理论)
规范化(社会学)
数据库
计算机网络
操作系统
工程类
机械工程
社会学
人类学
作者
Byeonghui Jeong,Jueun Jeon,Young‐Sik Jeong
出处
期刊:IEEE Transactions on Cloud Computing
[Institute of Electrical and Electronics Engineers]
日期:2023-10-01
卷期号:11 (4): 3497-3509
标识
DOI:10.1109/tcc.2023.3292378
摘要
The container resource autoscaling technique provides scalability to cloud services composed of microservice architecture in a cloud-native computing environment. However, the service efficiency is reduced as the scaling is delayed because dynamic loads occur with various workload patterns. Furthermore, estimating the efficient resource size for the workload is difficult, resulting in resource waste and overload. Therefore, this study proposes high-performance resource management (HiPerRM), which stably and elastically manages container resources to ensure service scalability and efficiency even under rapidly changing dynamic loads. HiPerRM forecasts future workloads using a sample convolutional and interaction network (SCINet) model applied with the reversible instance normalization (RevIN) method. HiPerRM generates a resource request with an elastic size based on the forecasted CPU and memory usage, and then efficiently adjusts the pod's resource request and the number of replicas via HiPerRM's VPA (Hi-VPA) and HiPerRM's HPA (Hi-HPA). As a result of evaluating the performance of HiPerRM, the average resource utilization was improved by approximately 3.96–34.06% compared to conventional autoscaling techniques, even when the resource size was incorrectly estimated for various workloads, and there were relatively fewer overloads.
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