Computational Task Offloading in Mobile Edge Computing using Learning Automata
Zahir Abbas, Jun Li, Nagendra Prasad Yadav, Irfan Tariq · 2018
The widespread diffusion of mobile devices is triggering an exponential growth of mobile data causing an offloading problem. In this paper, a model has been proposed to investigate computing task offloading problem of small base stations (SBSs). We formulate an optimization problem to minimize utility function for energy and execution time consumptions, where mobile devices (MDs) can select local computation or offloading to SBSs. We use discrete generalist pursuit algorithm (DGPA) based on learning automata (LA) to optimize the computation allocation. First, we examine a faster-converging of discrete generalist pursuit algorithm (DGPA) for LA is based on the concept of conditional inaction discrete generalist pursuit algorithm (CI-DGPA). Then, we design a reward function for the selection of optimal allocation of SBSs with minimum offloading latency. After convergence to optimal allocation, the offloading computing task of MDs has been performed to SBSs with minimum weighted sum of time and energy consumption. Finally, numerical results demonstrate the best improvement of DGPA algorithm.