Protected Framework Employing Flexible and Optimum Arrangement in Cloud Computing

ABSTRACT The idea of cloud computing (CC) originates from making resources available for task execution. Cloud computing is an advancement of supercomputing. The main challenges in CC are the varying resources and workloads, leading to the need for efficient tasking and scheduling. Distributed task scheduling can help us better understand workflow scheduling; autonomous task scheduling that accounts for security and execution time, mutual trust among system participants, better energy efficacy, and system utilization, among other aspects. The MBABE technique expanded as multi‐level blockchain attribute‐based encryption, which is used to ensure data security. ABE is a combined encryption method that can be effectively utilized for security and access control. Additionally, a new algorithm is presented, which is optimized with a convolutional neural network and snoop slingshot spider optimization (CNN‐S3SO). The cost functions are minimized using this S3SO for multipleusers in multiple cloud tasks computing progress. The technique relies on the actions of the arachnid in capturing targets and is utilized to arrange tasks for optimal throughput and minimal makespan. It is additionally recommended as a means of achieving convergence in a brief time frame. In addition, a protocol reliant on blockchain technology is utilized to encode data, ensuring secure transmission. Ultimately, the method is tested through a cloudlet simulator, and its efficiency is assessed through the analysis of the results. The outcome of the resource utilization rate is around 98%. It demonstrates that this methodology outperforms other task scheduling methods.