User incentive model and its optimization scheme in user-participatory fog computing environment

Although fog computing is recognized as an alternative computing model to cloud computing for IoT, it is not yet widely used. The replacement of network equipment is inevitable to implement fog computing; however, the entity in charge of replacement, requiring high cost, is unclear, and also the entity in charge of operating of the infrastructure is unclear. To solve these feasibility problems, we propose an incentive-based, user-participatory fog computing architecture. In terms of inducement of user participation on the proposed architecture, first, users are classified into four categories according to their tendencies and conditions, and the types of incentives, which are paid as a compensation for participation, the payment standard, and the operation model are presented in detail. From the perspective of fog service instance deployment, the instances should be deployed to reasonably minimize the incentives paid to the participating users of the proposed architecture, which is directly linked to maximizing the profitability of the infrastructure operator, while maintaining the performance. The optimization problem for the instance placement to achieve above design goal is formulated with a mixed-integer nonlinear programming, and then linearized. The proposed instance placement scheme is compared with several schemes through simulations based on actual service workload and device power consumption.