Bargaining Game Based Offloading Service Algorithm for Edge-Assisted Distributed Computing Model

Computation offloading is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we propose a new computation offloading model for the 5G networks and beyond. Based on the edge computing platform, intensive computing tasks can be partially offloaded from local devices to edge clouds to supplement the computation capability of resource-limited devices. This approach leverages the edge server’s idle computing power to assist individual devices in model training. To implement control decision algorithms for the distributed computing process, we adopt the concepts of different bargaining solutions for the dynamic offloading services. According to the cooperative game theory, the proposed method can maximize the full synergy that gives mutual advantages for devices and edge clouds while improving the system efficiency. Therefore, we can take various benefits to reach a fair-efficient consensus under the edge-assisted distributed computing system environment. Finally, experimental results demonstrate the effectiveness of our bargaining based computation offloading scheme by comparing with the existing state-of-the-art distributed computing protocols; we can accelerate training process thanks to our efficient bargaining approach.