IJBTT

Enhancing Web Application Using Adaptive Containerized Application Placement Based on Clustering and Content Caching in The Cloud Environment

Enhancing Web Application Using Adaptive Containerized Application Placement Based on Clustering and Content Caching in The Cloud Environment
	© 2022 by IJETT Journal
	Volume-70 Issue-1
	Year of Publication : 2022
	Authors : Mohamed. I.El-Shenawy, Hayam Mousa, Khaled M. Amin
	DOI :  10.14445/22315381/IJETT-V70I1P218

How to Cite?

Mohamed. I.El-Shenawy, Hayam Mousa, Khaled M. Amin, "Enhancing Web Application Using Adaptive Containerized Application Placement Based on Clustering and Content Caching in The Cloud Environment," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 162-169, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P218

Abstract
Datacenter traffic increases from day to day due to the massive increase of web applications hosted on the Internet. Some tools are used in resource management and capacity assessment in order to preserve a good performance for these applications. The container is a new trend for packaging and deploying micro-service-based applications. It is widely used to improve performance and achieve high user satisfaction. Autoscaling has become a vital feature in such applications’ performance. This article targets to improve the quality of service through increasing resources utilization and reducing the number of application container kills and recreation. These targets can be achieved through dependency on the healthier nodes that have adequate resources. Machine Learning classification algorithms are used to predict healthy hosts. Then, a clustering algorithm is used to cluster healthy nodes into groups of containers workers` hosts based on their CPU and RAM utilization. In addition, content caching service has been integrated to improve application performance. This service decreases the network traffic to hosts nodes which subsequently decreases the required resources to handle these requests. The results ensure that the proposed model can achieve lower node failure with 33% of the default system. It also saves around 36% of bandwidth.

Keywords
Cloud computing, containers, autoscaling, virtualization, orchestration, machine learning.

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