基于稳定匹配的多用户任务卸载策略

随着许多计算密集型应用的出现，移动设备因其有限的计算能力无法满足用户时延、能耗等需求。移动边缘计算（MEC）通过无线信道将用户的任务计算卸载到MEC服务器，从而显著减少任务响应时延和能耗。针对多用户任务卸载问题，提出了基于稳定匹配的多用户任务卸载策略（MUTOSA），在保证用户的时延要求下达到能耗最小化。首先，在综合考虑时延与能耗的基础上，对独立任务场景下的多用户任务卸载问题进行建模；然后，基于博弈论的稳定匹配中的延迟接收思想，提出了一种调整策略；最后，通过不断迭代，解决了多用户任务卸载问题。实验结果表明，该策略相较于基准策略和启发式策略能够满足更多用户的时延要求，平均提高约10%的用户满意度，并能减少约50%的用户设备总能耗。所提策略在保证用户时延要求的同时有效地减少了能耗，可以有效地提高用户对于时延敏感型应用的体验。

Multi-user task offloading strategy based on stable allocation

With the emergence of many computation-intensive applications, mobile devices cannot meet user requirements such as delay and energy consumption due to their limited computing capabilities. Mobile Edge Computing (MEC) offloads user task computing to the MEC server through a wireless channel to significantly reduce the response delay and energy consumption of tasks. Concerning the problem of multi-user task offloading, a Multi-User Task Offloading strategy based on Stable Allocation (MUTOSA) was proposed to minimize energy consumption while ensuring the user delay requirement. Firstly, based on the comprehensive consideration of delay and energy consumption, the problem of multi-user task offloading in the independent task scenario was modeled. Then, based on the idea of delayed reception in the stable allocation of game theory, an adjustment strategy was proposed. Finally, the problem of multi-user task unloading was solved through continuous iteration. Experimental results show that, compared with the benchmark strategy and heuristic strategy, the proposed strategy can meet the delay requirements of more users, increase user satisfaction by about 10% on average, and reduce the total energy consumption of user devices by about 50%. It shows that the proposed strategy can effectively reduce energy consumption with ensuring the user delay requirement, and can effectively improve the user experience for delay-sensitive applications.