M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling solving multi-objective task computing environments caused by widely distributed resources strong load uncertainties. Firstly, a unloading model based on dynamic priority adjustment is proposed. Secondly, optimization constructed for model, which optimizes time delay energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight external archives) can effectively handle problems Pareto fronts reduce response consumption costs scheduling.