Energy-deadline optimization with minimal task failure aware task partitioning model in heterogeneous cloud computing framework

The central processing Unit (CPU) and graphical processing unit (GPU) will be used in high-performance computing (HPC) to provide scalable and effective computing paradigms for data-intensive scientific workloads. Nonetheless, energy use is a significant aspect that should be considered due to rising operational costs and green computing standards. Scientific workload scheduling is a challenging task since heterogeneous cloud computing (HCC) infrastructures consume more energy, which raises carbon emissions and lowers the reliability of the infrastructures. Although using the dynamic voltage-frequency scaling (DVFS) approach can improve the energy management of cloud infrastructure, it also decreases dependability and increases the error rate of workload scheduling on a CPU-GPU HCC architecture; thus, reducing task failure and minimizing energy are core issues that the current work addresses. The work first introduces the energy-deadline-aware task scheduling optimization (EDATSO) technique; secondly, it introduces the task-failure minimization-aware optimal scheduling (TFMOS) technique for the execution of scientific workflows. Simulation study demonstrates EDATSO reduces energy usage by 40.3 %, and 33.12 %, reduces makespan by 90.35 %, and 53.56 %, and overhead of additional energies used due to task failures by 95.56 %, 87.59 % as compared to energy minimized scheduling (EMS), multi-objective prioritized workflow scheduling through deep reinforcement learning (MOPWSDRL) for realistic scientific workloads, respectively. Further, TFMOS reduces energy usage by 40.33 %, and 46.4 %, reduces makespan by 90.4 %, and 53.95 %, and overhead of additional energies used due to task failures by 95.58 %, 87.61 % as compared to EMS, MOPWSDRL for realistic scientific workloads, respectively.