Hybrid evolutionary scheduling for energy-efficient fog-enhanced internet of things

In recent years, the rapid development of the Internet of Things (IoT) has produced a large amount of data that needs to be processed in a timely manner. Traditional cloud computing systems can provide us with plentiful resources to process such data. However, the increasing requirements of IoT applications on data privacy, energy consumption savings and location-aware data processing pushes the emergence and the interplay of fog computing and cloud computing. This paper examines the resource scheduling issue under such a system to minimize makespan and energy consumption. A multi-objective estimation of distribution algorithm (EDA) as well as a partition operator is adopted to divide the graph and determine the task processing permutation and processor assignment. Single and multiple application simulation were both conducted. The comparative results show that the Pareto set produced by our proposed algorithm is able to dominate a large proportion of those solutions by the heuristic method and the simple EDA under single application simulation. When it comes to multi-application simulation, IoT devices can have a much longer lifetime with our proposed scheduling algorithm as well having similar performance to the other algorithms on fog node energy consumption and much better on makespan.