Optimization Method of Fog Computing High Offloading Service Based on Frame of Reference

Abstract

The cost of offloading tasks is a crucial parameter that influences the task selection of fog nodes. Low-cost tasks can be completed quickly, while high-cost tasks are rarely chosen. Therefore, it is essential to design an effective incentive mechanism to encourage fog nodes to actively participate in high-cost offloading tasks. Current incentive mechanisms generally increase remuneration to enhance the probability of participants selecting high-cost tasks, which inevitably leads to increased platform costs. To improve the likelihood of choosing high-cost tasks, we introduce a frame of reference into fog computing offloading and design a Reference Incentive Mechanism (RIM) by incorporating reference objects. Leveraging the characteristics of the frame of reference, we set an appropriate reference task as the reference point that influences the attraction of offloading tasks to fog nodes and motivates them towards choosing high-cost tasks. Finally, simulation results demonstrate that our proposed mechanism outperforms existing algorithms in enhancing the selection probability of high-cost tasks and improving platform utility.

Keywords

• fog computing
• optimization method
• frame of reference
• task offloading rate
• platform utility