.png){kind=icon}
返回
ARAScaler: Adaptive Resource Autoscaling Scheme Using ETimeMixer for Efficient Cloud-Native Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/TSC.2024.3522815.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The container resource autoscaling techniques offer scalability and continuity for microservices operating in cloud-native computing environments. However, they manage resources inefficiently, causing resource waste and overload under complex workload patterns. In addition, these techniques fail to prevent oscillations caused by dynamic workloads, increasing the operational complexity. Therefore, we propose an adaptive resource autoscaling scheme (ARAScaler) to ensure the stability and resource efficiency of microservices with minimal scaling events. ARAScaler predicts future workloads using enhanced TimeMixer (ETimeMixer) applied with the convolutional method. Additionally, ARAScaler segments the predicted workload to identify burst, nonburst, dynamic, and static states and scales by calculating the optimal number of container instances for each identified state. The offline simulation results using seven cloud-workload trace datasets demonstrate the high prediction accuracy of ETimeMixer and the superior scaling performance of ARAScaler. The ARAScaler achieved a resource utilization of approximately 70% or higher with few updates and recorded the fewest resource overload instances compared to existing container resource autoscaling techniques.
Keyword:
Containers
Microservice architectures
Oscillators
Forecasting
Vectors
Predictive models
Market research
Data preprocessing
Computer architecture
Computational modeling
Cloud-native computing
microservice
container resource autoscaling
time-series forecasting
期刊
IF:
5.8
论文数:
2.2K
被引数:
6.5K
机构
引用论文
Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model使用联邦学习模型在基于容器的边缘计算中用于web应用程序的主动自动扩展技术
PREAI
分享收藏分享收藏Stable and efficient resource management using deep neural network on cloud computing云计算环境下基于深度神经网络的稳定高效资源管理.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
Cloud-Native Computing: A Survey From the Perspective of Services云原生计算: 服务视角的综述PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model使用集成模型的生产应用程序的主动自动扩展方法IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容