Resource allocation method for minimizing time in multi-task edge computing
Aiming at the characteristic of a certain dependency relationship existing among multiple tasks in the edge computing system,a resource allocation strategy for minimizing the total computing time is investigated in this paper.Sequential dependency relationship among multiple tasks is taken into account.Multiple tasks of the user are offloaded in sequence.When the current task completes offloading,the next task can be offloaded without waiting for the current task to finish computing.By using a two-tier offloading strategy,user can first offload task to small base station(SBS),and when the edge server in SBS has insufficient computing capacity,SBS will offload the part of task to the edge server in macro base station.The joint optimization of user association,resource allocation of computation resources and the transmitting power of user are formulated to minimize the total computation time of the multi-task edge computing(MEC)system.A suboptimal solution is obtained by adopting a quantum-behaved particle swarm optimization(QPSO)algorithm.Simulation results show that the QPSO algorithm has less total computation time compared with the standard particle swarm optimization algorithm and the other benchmark strategies.
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