.png){kind=icon}
返回
An Efficient Load Prediction-Driven Scheduling Strategy Model in Container Cloud
.png){kind=icon}
OA
AI
DOI：10.1155/2023/5959223.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine-grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN-BiGRU-Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine-grained manner based on the load prediction value in the next time window, while the established domain-based container selection method is employed to facilitate the coarse-grained online migration of containers. Experiments conducted using public datasets and open-source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.
期刊
IF:
3.7
论文数:
3.1K
被引数:
8.1K
机构
引用论文
.png){kind=icon}
Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications' QoS基于ARIMA模型的工作负载预测及其对云应用QoS的影响
OAAI
OAAI分享收藏分享收藏Inhibition of delta-like ligand 4 decreases Th1/Th17 response in a mouse model of multiple sclerosis在多发性硬化症小鼠模型中，delta样配体4的抑制可降低Th1/Th17反应
PREAI
分享收藏分享收藏分享收藏
查看更多内容