SECURE AND ENERGY-EFFICIENT TASK SCHEDULING IN CLOUD CONTAINER USING VMD-AOA AND ECC-KDF.

The surge of lightweight containers in cloud storage, propelled by technological advancements, offers dependable services. However, the inherent security concerns arising from shared access to the host Operating System (OS) necessitate an effective solution. This paper introduces an energy-efficient and secure scheduling approach to tackle this issue. Users register task requests with a resource manager, which collects and preprocesses data to eliminate redundancies. The Levenberg-Marquardt Multi-Layer Perceptron Neural Network (LM-MLPNN) optimizes container resource utilization by analyzing user requests. The Homography Transform-based K-Mode Algorithm (HT-KMA) facilitates efficient clustering through attribute extraction. To address imbalances, the Weighted Round Robin (WRR) technique is employed. An optimal container, selected by the Variational Mode Decomposition-based Archimedes Optimization Algorithm (VMD-AOA), undergoes Elliptic Curve-based Key Derivation Function (EC-KDF) for enhanced security before being transmitted to the resource manager. Experimental results demonstrate the superiority of our methodology over existing algorithms. [ABSTRACT FROM AUTHOR]