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

Abstract

Since the cloud storage system offers reliable storage services, it is facing an exponential shift towards lightweight containers due to advancements in technology. Nevertheless, for performing numerous applications, common access to the host Operating System (OS) in the container affected its security. Thus, this paper proposes an energy-efficient and secured scheduling approach. Primarily, multiple users send task requests to the resource manager by registering their details. Then, data are collected from the registered user; also, for removing the repeated requests, pre-processing takes place. Next, by employing the Levenberg-Marquardt Multi-Layer Perceptron Neural Network (LM-MLPNN) technique, the nature of the request from the user is checked to aid in the efficient utilization of container resources. By utilizing the Homography Transform-based K-Mode Algorithm (HT-KMA), the attributes are extracted from the normal user for the efficient clustering process. Then, by deploying the Weighted Round Robin (WRR) technique, the imbalance in the container environment is avoided. An optimal container is selected using the Variational Mode Decomposition-based Archimedes Optimization Algorithm (VMD-AOA) method based on the clustered tasks, and is effectively secured using Elliptic Curve based Key Derivation Function (EC-KDF) and transferred to the resource manager. To perform the required tasks, the resource manager in turn forwards the selected container to the corresponding user. As per the experimental outcomes, when analogized to other well-known algorithms, the proposed methodology achieves better performance.