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A Data Transmission Strategy with Energy Minimization Based on Optimal Stopping Theory in Mobile Cloud Computing

机译:基于最优停止理论在移动云计算中的能量最小化数据传输策略

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Considering the widespread use of mobile devices and the increased performance requirements of mobile users, shifting the complex computing and storage requirements of mobile terminals to the cloud is an effective way to solve the limitation of mobile terminals, which has led to the rapid development of mobile cloud computing. How to reduce and balance the energy consumption of mobile terminals and clouds in data transmission, as well as improve energy efficiency and user experience, is one of the problems that green cloud computing needs to solve. This paper focuses on energy optimization in the data transmission process of mobile cloud computing. Considering that the data generation rate is variable, because of the instability of the wireless connection, combined with the transmission delay requirement, a strategy based on the optimal stopping theory to minimize the average transmission energy of the unit data is proposed. By constructing a data transmission queue model with multiple applications, an admission rule that is superior to the top candidates is proposed by using secretary problem of selecting candidates with the lowest average absolute ranking. Then, it is proved that the rule has the best candidate. Finally, experimental results show that the proposed optimization strategy has lower average energy per unit of data, higher energy efficiency, and better average scheduling period.
机译:考虑到移动设备的广泛使用和移动用户的性能要求,将移动终端的复杂计算和存储要求转换为云是解决移动终端限制的有效方法,这导致了移动的快速发展云计算。如何减少和平衡数据传输中移动终端的能量消耗和云,以及提高能源效率和用户体验,是绿云计算需要解决的问题之一。本文侧重于移动云计算数据传输过程中的能量优化。考虑到数据生成率是可变的,由于无线连接的不稳定性,结合传输延迟要求,提出了一种基于最佳停止理论的策略,以最小化单元数据的平均传输能量。通过构建具有多个应用的​​数据传输队列模型,通过使用秘书问题选择具有最低平均绝对排名的候选者的秘书问题提出了优于顶部候选的准入规则。然后,证明该规则具有最佳候选人。最后,实验结果表明,所提出的优化策略每单位数据的平均能量较低,能量效率较高,平均平均调度期限。

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