الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
In chapter 1 a general introduction to the to the thesis topic and the motivation of the
thesis topic then shows thesis contribution and brief outline of thesis content.
Chapter 2 - E-learning with Virtualization and containers: a brief background about
webservices then shows its advantage and benefits then moved to show how was the
digital transformation has affected on the performance of eLearning systems as a web
application. After that provided overviewed traditional virtualization and
virtualization core components with focus on hypervisors, virtual machines
architecture and show how this traditional virtualization technology is no longer
suitable with new generation of application deployment in favor if new virtual
technology called containers which proven high impact on deployed application and
comparison between virtual machines and containers. Discuss how the cloud and its
architecture have benefited from containers in deploying high performance
applications with less hardware consumable and discuss the container orchestration
platform Kubernetes which depended on in this research to deploy the web
application.
Chapter 3 – State of the Art: In this chapter the related work to this thesis is discussed
in two parts. In part one, containers autoscaling issue. In part two, discussing the
related work regarding the containers scheduling issue and how the metaheuristics
optimization algorithms were used to enhance the resource scheduling optimization.
Chapter 4 - Secure Based Predictive Autoscaling Model For containerized application:
in this chapter started to show the steps followed to deploy the Kubernetes cluster with
monitoring application Prometheus and visualization of monitored resources and then
deploy web application were started to apply the enhancement methos of this research.
Then discussed the contribution enhancement methods was followed to enhance the
deployed containerized application. Machine learning module used to predict healthy
hosts before deploying web application to decrease the fails possibilities of containers
deployments. Clean the incoming traffic to containers web application to save the
required resources to handle this fake workload, apply content caching to save the
resources and internet bandwidth consumed. Predict application future workload and
apply the autoscaling required before the sudden workload to avoid application
bottleneck.
Chapter 5 - Hybrid metaheuristic Multi-Objective Optimization Scheduling Method
For containerized application: in this chapter briefly overviewed the container
scheduling and the classification of Scheduling optimization algorithms then show
how metaheuristics optimization algorithms was used to optimize the container
scheduling and aiming to maximize the nodes resources utilizations and decrease
costs.
Chapter 6 – Concludes this study. It becomes clear that the proposed container
autoscaling using Machine learning forecasting and using metaheuristic to optimize
Kubernetes scheduling process have achieved high performance and was able to
achieve high resource utilization and able to save the cost spent.