Multitask offloading algorithm under heterogeneous edge cloud architecture
To run computing-intensive and delay-sensitive applications on terminal devices with limited resources and to reduce system time delay and energy consumption,an edge cloud heterogeneous network model is construc-ted.In addition,a hierarchical particle swarm optimization with genetic algorithm(H-PSOGA)for multitasking of-floading optimization is proposed for multitasking computational offloading through edge devices,such as drones,roadside units,and vehicles,as well as edge cloud servers.The H-PSOGA combines particle swarm and genetic algorithms in a serial and then parallel manner.The genetic algorithm is used to optimize the particle swarm through operations such as fitness value sequencing calculation,population selection,multipoint crossover,and reverse mu-tation to compensate for the defects in the particle swarm algorithm.These issues include premature convergence and local optima.The test analysis of six standard test functions and the result of simulation comparison with the baseline scheme show that with a large number of users,the average cost can be reduced by 26%to 43%,H-PSOGA can effectively improve convergence accuracy reduce system overhead.
mobile edge computingheterogeneous networkedge nodetask offloadingparticle swarm algorithmgenetic algorithmmultiobjective optimizationstandard test function