Optimizing Cloud Security in Dynamic Environments Through Adaptive Workload Management Algorithm

With the growing interest of researchers in the area of cloud computing, security emerges as a major area of concern. As the complexity of cloud environments grows and the dependency on cloud services deepens across various sectors, ensuring robust security measures is crucial. This study proposes an innovative approach to enhance cloud security through efficient workload scheduling strategies. Leveraging insights from existing studies on green cloud computing, clonal selection algorithms, and energy-efficient resource allocation, the research introduces a novel security-aware workload organizing algorithm. The proposed algorithm aims to optimize both energy consumption and security in cloud data centers. Through comprehensive simulations on the Cloud Sim platform, the results demonstrate the algorithm's efficiency in reducing energy consumption while fortifying security measures. Moreover, the present study contributes to the evolving field of cloud security, offering a promising avenue for shielding data in dynamic cloud environments.