Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing

Fog computing is an interesting technology aimed at providing various processing and storage resources the IoT networks’ edge. Energy consumption one of essential factors that can directly impact maintenance cost CO2 emissions fog environments. be mitigated by effective scheduling approaches, in which tasks are going to mapped on best possible regarding some conflicting objectives. To deal with these issues, we introduce opposition-based hybrid discrete optimization algorithm, called DMFO-DE. For this purpose, first, a Opposition-Based Learning (OBL) version Moth–Flame Optimization (MFO) algorithm provided, it then combined Differential Evolution (DE) improve convergence speed prevent local optima problem. The DMFO-DE employed for scientific workflow environments using Dynamic Voltage Frequency Scaling (DVFS) method. Heterogeneous Earliest Finish Time (HEFT) used find execution order workflows. Our approach mainly tries decrease process’s energy minimizing applied Virtual Machines (VMs), makespan, communication between dependent tasks. evaluating performance proposed scheme, extensive simulations conducted workflows four different sizes. experimental results indicate outperform other metrics such as number VMs, consumption.