An Advanced Cloud Data Streaming Framework for Optimized Container Resource Allocation, Job Scheduling, And Security Enhancement

Abstract

In the realm of cloud data streaming, the central concerns are Container resource allocation and job scheduling. Cloud infrastructure relies on container virtualization to facilitate construction and migration processes. Previous models have employed migration techniques to manage cloud container allocation and resource scheduling, but these come at the cost of increased response time and network traffic. To address these challenges, a novel approach is introduced, the Reduced Optimal Migration model (ROM). This model selectively triggers migration processes based on recommendations, optimizing resource allocation through a Machine Learning (ML) Algorithm. Job scheduling is enhanced through a dedicated Task Scheduling Algorithm. For robust data security during migration, a security-based technique is implemented in the 'Security-based Container Scheduling Model,' which ensures data integrity and safeguards against attacks. This system operates seamlessly online and offline, utilizing Edge Computing. During offline periods, defensive containers maintain data security until the system owner restores online connectivity. This holistic framework proves highly effective in resolving complex issues associated with large-scale optimization of resource allocation, migration, and security. Empirical results confirm its efficiency and security enhancements. The proposed work introduces an advanced cloud data streaming framework that optimizes container resource allocation and job scheduling while enhancing security during migration. It proves effective in addressing the challenges inherent in large-scale cloud data streaming processes.