Intelligent Task Offloading for Smart Devices in Mobile Edge Computing

Mobile edge computing (MEC) is used for compu-tationally complex applications by offloading it to the nearby edge server either partially or entirely. The problem arises of selecting whether the component is to be offloaded to the mobile edge server (MES) for execution, or it needs to be executed locally. Therefore, we propose a time-efficient decision offloading scheme (TEDOS) to derive a data set and train an artificial neural network (ANN) on the derived data set. TEDOS provide the smart decision on the optimal permutation of the divided components based on delay. We developed a mathematical model for delays in communication, execution and component queuing. We obtained a final delay for all possible permutations of component offloading policies. Our model obtained 91 % accurate results as compared to the existing schemes. The simulation result shows that our proposed model outperforms the state-of-the-art.