SBPAM: Secure Based Predictive Autoscaling Model For containerized application

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
because of 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. 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. Hence, the Auto-Scaler
module begins to auto-scale the number of resources to meet
the cluster's workload, leading to efficient use of resources.