Node cooperation for workload offloading in a fog computing network via multi-objective optimization

The number of devices connected to the internet is increasing at a tremendous rate. Connecting intelligent devices has created a new paradigm called the Internet of Things (IoT). Due to the significant volume of data generated in the IoT, cloud infrastructure alone cannot process this data. Thus, fog computing has been proposed to supplement the cloud. One of the critical issues in fog computing is deciding how much of the workload should be offloaded to the cloud. In this paper, a multi-objective optimization problem that considers cooperation between fog nodes is presented. The goal is to minimize the response time, energy cost, and rental cost of cloud resources. This multi-objective optimization problem is transformed into a single-objective optimization problem which is solved using the distributed Alternating Direction Method of Multipliers (ADMM) algorithm. Results are presented which show that this distributed algorithm converges to an optimal solution which provides a tradeoff between the total response time, total energy cost in the fog nodes, and total rental cost of cloud resources. It is shown to provide better performance compared to the well-known adversary algorithm.