| SBPAM: Secure Based Predictive Autoscaling Model For containerized application |
| Paper ID : 1012-ICCI2021 (R1) |
| Authors: |
|
Mohamed I. Elshenawy *1, Hayam MOUSA2, khaled M. Amin2 1Information Technology Department , Canadian International College, Cairo, Egypt 2Faculty of Computers and Information Menoufia University Shibin El Kom, Egypt |
| Abstract: |
| During the past few years, the virtual technology used in the cloud has become unsuitable for service delivery, with the emergence of containers technology and the spread of its use throughout a wide range of cloud service providers due to the ease of use and provision of the resources used. The institutions have become widely seeking to develop this technology to suit the different needs to provide a good service for the end-user. Cost-efficient resource provisioning based on real-time changes of workloads is one of the important topics in auto-scaling the VMs and containers in cloud environments to achieve the quality of service. In this paper, we use machine learning to improve the container orchestration process. Our approach focuses on getting containers cluster resources idle as much as can by scanning and clearing malicious and unwanted fake load to enhance the workers load then using machine learning models to predict loads in advance so the Auto-Scaler module begins to auto-scale the number of resources to meet the cluster’s workload which leads to efficient use of resources. |
| Keywords: |
| Cloud computing, containers, autoscaling, virtualization, orchestration, machine learning. |
| Status : Paper Accepted |
