负载预测在双通道剩余自我注意时间卷积网络与重量适应更新在云计算中的负载预测
Jiang Lin1, Yepeng Guan1,2,3
1School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
准确的云计算负载预测对于及时的容器集群响应至关重要. 一种新方法,DSTNW,使用双通道自我注意时间卷积网络与重量自适应更新,以提高预测准确度.
科学领域:
- 云计算 云计算 云计算
- 机器学习 机器学习
- 资源管理 资源管理
背景情况:
- 云资源需求的快速波动需要及时进行容器集群扩展,以提高服务质量.
- 准确的资源负载预测仍然是广泛采用云计算的一个重大挑战.
研究的目的:
- 提出一种新的方法,DSTNW,用于快速准确的云计算负载预测.
- 为了提高容器集群扩展在云环境中的响应能力和精度.
主要方法:
- 开发了一种双通道时间卷积网络 (DTN) 用于长期依赖性捕获和特征提取.
- 集成了一个剩余的时间自我注意机制 (SM),以专注于重要的特征.
- 将DTN和SM结合成一个双通道的残余自我注意时间卷积网络 (DSTN).
- 实施了适应性权重策略,以优化单个和堆叠的DSTN准确性.
主要成果:
- 与现有的最先进的方法相比,DSTNW方法显示出更高的预测性能.
- 在容器数据集上实现了24.16%的平均改进,在谷歌数据集上达到30.48%.
结论:
- 拟议的DSTNW方法在云计算负载预测方面取得了重大进展.
- 这种方法提高了集装箱集群适应动态资源需求的能力,确保服务质量.
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