Enhanced Task Scheduling in Cloud Computing Using the ESRNN Algorithm: A Performance-Driven Approach

One key component of cloud computing that significantly affects performance is task scheduling. Quality of Service (QoS) in networking and the burgeoning information processing economy have spurred interest in the dynamic task scheduling challenge worldwide. Task scheduling is a complicated topic that has been categorized as NP-hard. It is also more difficult to strike a balance and reap the rewards of every facet of cloud computing because the majority of activities in complex environments are frequently managed using dynamic online task scheduling. This manuscript presents an approach for task scheduling in cloud computing technology for enhancing efficiency in enterprise environments. The proposed algorithm is an Effective Residual Self-Attention Recurrent Neural Network (ESRNN) Algorithm. The proposed method's primary goal is to improve system reliability, enhance efficiency, and provide better load standard deviation, make span, and throughput. The issue of managing Directed Acyclic Graph (DAG) jobs in a cloud computing context is resolved by the ESRNN algorithm. The performance of the proposed technique is implemented in the MATLAB platform and is compared with various existing approaches. The proposed approach determines better outcomes compared to existing methods such as Data aware, First-Come-First-Served (FCFS) and Round Robin (RR). In the existing method, throughput is 1, 1.2, 1.4 s, and then the proposed method throughput is 1.6 s. Based on the results, it can be concluded that the proposed approach has higher throughput compared to existing techniques.