To solve the current problems of inefficiency,low utilization of intensive network resources,and high sys-tem cost in handling multi-user applications,a multi-user fine-grained task offloading scheduling approach under cloud-edge-end collaboration was proposed. Latency,energy consumption,and server rental costs were jointly consid-ered. Application tasks were firstly divided and subtask priorities were designed. Then,a multi-user subtask schedul-ing scheme was proposed and an improved simulated annealing particle swarm algorithm was designed to minimize the total system cost to achieve the optimal offloading decision. Experimental results show that the proposed method reduces the total cost by at least 12.28％ and 7.42％ compared to other methods such as particle swarm and simulated annealing binary particle swarm,respectively.
维普
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