.png){kind=icon}
返回
Stable and efficient resource management using deep neural network on cloud computing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.11.089.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Resource management autoscaling in a cloud computing service guarantees the high availability and extensibility of applications and services. Horizontal pod autoscaling (HPA) does not affect the executed tasks but also has the disadvantage that it cannot provide immediate scaling. Furthermore, scale down is not possible if excess resources are allocated, because it is difficult to identify the amount of resources required for applications and services; thus resources are wasted. Therefore, this study proposes Proactive Hybrid Pod Autoscaling (ProHPA), which immediately responds to irregular workloads and reduces resource overallocation. ProHPA uses a bidirectional long short-term memory (Bi-LSTM) model applied with an attention mechanism for forecasting future CPU and memory usage that has similar or different patterns. Reducing excessive resource usage with vertical pod autoscaling (ReVPA) adjusts the overallocation of resources within a pod by forecasted resource usage. Lastly, prevention overload with HPA (PoHPA) immediately performs resource scaling by using forecasted resource usage and pod information. When the performance of ProHPA was evaluated, CPU and memory average utilization were improved by 23.39% and 42.52%, respectively, compared with conventional HPA when initial resources were overallocated. In addition, ProHPA did not exhibit overload compared to conventional HPA when resources are insufficiently allocated.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Cloud computing
Resource management
Hybrid pod autoscaling
Resource usage forecasting
Attention-based Bi-LSTM
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
分享收藏Respiratory syncytial virus nonstructural proteins 1 and 2 are crucial pathogenic factors that modulate interferon signaling and Treg cell distribution in miceVirologyOAAI
VirologyIF0
OAAI分享收藏Ensemble deep neural network based quality of service prediction for cloud service recommendationNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容