Multi-objective IoT Application Provisioning with Optimized Delay and Cost in Fog Computing

Fog computing brought a new collaborative computing model to make the growing of IoT possible. It provides the possibility to satisfy IoT applications needs by utilizing computational resources at the edge of the network. Most of the works have been done in researches, are trying to maximize or minimize one objective in different resource management problems. This approach could have some unwanted influence on other decision making aspects. This paper presents a multi-objective framework to find eligible fog nodes to dynamically deploy the IoT applications on them. The proposed framework could be employed to achieve a trade-off between the cost of resources and average service delay. The multi-objective dynamic service provisioning (MDSP) problem is formulated as a mixed-integer linear programming (MILP) model and the weighted goal programming is applied to solve the multi-objective problem. In addition, the Pareto front has been discussed and different simulations have been proposed in order to show the importance of using multi-objective solutions for service provisioning in fog computing.