INNOVATIVE DATA MANAGEMENT IN CLOUD-BASED COMPONENT APPLICATIONS: A DUAL APPROACH WITH GENETIC ALGORITHMS AND HEFT SCHEDULING

Abstract

The work integrates Heterogeneous Earliest Finish Time (HEFT) scheduling with Genetic
Algorithms (GAs) to propose a dual strategy to optimizing data management in cloud-based component
applications. Iteratively optimizing difficult problems, GAs draw inspiration from natural selection, whereas
HEFT effectively schedules jobs by reducing the total completion time across heterogeneous systems. Through
better resource usage, decreased latency, and enhanced data security via optimal encryption procedures, the
integrated strategy improves cloud system performance. Based on a 93% accuracy rate in cloud data management
task optimization, the suggested strategy performs better than conventional approaches. Providing a strong
framework for securely and effectively managing activities and data, this dual approach tackles the inherent
complexity of cloud systems. The efficiency of this approach is demonstrated by performance measures such as
task completion time, resource usage, and encryption strength.