Efficient and secure task scheduling in cloud communication using hybrid convolutional neural network and enhanced encryption techniques

Cloud communication is a combination of distributed computing and parallel computing. Task scheduling is a major challenge in cloud communications due to the NP-completeness of cloud systems. To address this, various swarm intelligence-based approximation techniques have been developed. This paper proposes a novel method for efficient task scheduling with improved security in cloud computing. A Hybrid Convolutional Neural Network with Long Short-Term Memory (HCNN-LSTM) optimized using FABOA is proposed for task scheduling to maximize throughput and minimize make span. Additionally, an improved random bit-stuffing technique with a modified RSA algorithm ensures secure data transmission. A novel Hybrid Convolutional Neural Network with Long Short-Term Memory (HCNN-LSTM) algorithm which is optimized using FABOA is proposed, which complicates readability. While the introduction outlines general cloud computing challenges, it lacks a focused literature review that identifies specific gaps in existing work and clearly justifies the need for the proposed HCNN-LSTM-FABOA system. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption.