Dynamic voltage scaling based energy-minimized partial task offloading in fog networks

With the dynamic voltage scaling (DVS) technology, the terminal node (TN) can dynamically adjust its computational speed, thus providing a new way to save energy during task offloading in fog computing. Focusing on the scenario of one TN and multiple fog nodes (FNs), this paper proposed an Energy-Minimized Partial Task Offloading (EMPTO) scheme for the first time to reduce the overall energy consumption based on DVS technology. Firstly, by modeling the energy consumption and processing delay of task offloading, we formulated the problem of minimizing energy consumption. Then, using the variable substitution method, we transformed this energy minimization problem into a univariate optimization problem about the TN’s computational speed. By solving this problem, EMPTO gets the optimal TN’s computational speed, task offloading size between each pair of TN and FN, and the overall energy consumption. Finally, EMPTO selects the offloading scheme with the lowest overall energy consumption as the final scheme. Theoretical proof and simulation results show that EMPTO can achieve the minimum energy consumption by DVS technology under delay constraint.