.png){kind=icon}
.png){kind=icon}
Return
Auto-scaling containerized cloud applications: A workload-driven approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.simpat.2022.102654.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Today, cloud computing presents new business opportunities as it offers various technological advantages including elastic computing and efficient pricing strategies. Although, cloud users have access to large amount of resources, it is yet a challenging task to efficiently manage the hardware resources in a cloud environment. In this article, we present PACE (Performanceaware Auto-scaler for Cloud Elasticity), a framework for auto-scaling containerized cloud applications based on workload demand. The framework offers a) reactive auto-scaling using threshold-based rules to avoid application failures during intensive workload tasks and b) proactive auto-scaling using convolutional neural networks (CNN) and K-means to generate elastic scaling policies that incorporate future workload demands. The experimental analysis is based on the Yahoo! Cloud Serving Benchmark (YCSB) executed in Redis containers deployed on the Google Cloud Platform. The proposed framework can automatically adjust cloud resources to satisfy workload demand and ensure Quality of Service (QoS) requirements.
Keywords:
Cloud computing
Elastic scaling
Convolutional neural network
K-means
Time series analysis
Machine learning
Journal
IF:
4.6
Papers:
2.6K
Citations:
4.8K