Future Multi-access edge computing (MEC) systems are required to execute applications composed of dependent tasks under end-to-end delay requirements. Further, they are likely to rely on renewable (or green) energy sources that are limited and fluctuate over time. These conditions give rise to a fundamental optimization problem that involves determining how tasks should be offloaded across edge servers so that dependency and delay constraints are satisfied while maximizing green energy usage or reducing an operator’s reliance on brown energy. In addition, the problem involves how green energy is shared among servers. Henceforth, we address a novel problem, called Green Dependent Task Offloading (G-DTO), that aims to maximize green energy usage, subject to the following constraints: (i) each edge server has limited green energy and computational capacity, and (ii) each task of an application, with a specified size, energy, and computational requirement, must be executed by a given deadline. To determine the optimal solution, we outline a Mixed-Integer Linear Programming (MILP) model. We also outline a genetic-algorithm-based approach, called G-DTO/GA. The simulation results on 18 synthetic network scenarios with small problem instances show that G-DTO/GA achieves an average of 96.83% green energy usage, closely matching the optimal MILP solution while requiring only 9.93% of the MILP runtime. Further experiments on the same 18 synthetic network scenarios, but with large problem instances, show that G-DTO/GA sustains high green energy usage, averaging 90.10%.
Alawneh et al. (Sun,) studied this question.