Mobile cloud offloading migrates heavy computation from mobile devices to remote cloud resources or nearby cloudlets. It is a promising method to alleviate the struggle between resource-constrained mobile devices and resource-hungry mobile applications. Caused by frequently changing location mobile users often see dynamically changing network conditions which have a great impact on the perceived application performance. Therefore, making high-quality offloading decisions at run time is difficult in mobile environments. To balance the energy-delay tradeoff based on different offloading-decision criteria (e.g., minimum response time or energy consumption), an energy-efficient offloading-decision algorithm based on Lyapunov optimization is proposed. The algorithm determines when to run the application locally, when to forward it directly for remote execution to a cloud infrastructure and when to delegate it via a nearby cloudlet to the cloud. The algorithm is able to minimize the average energy consumption on the mobile device while ensuring that the average response time satisfies a given time constraint. Moreover, compared to local and remote execution, the Lyapunov-based algorithm can significantly reduce the energy consumption while only sacrificing a small portion of response time. Furthermore, it optimizes energy better and has less computational complexity than the Lagrange Relaxation based Aggregated Cost (LARAC-based) algorithm.

中文翻译：

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移动云卸载的节能决策

移动云卸载将大量计算从移动设备迁移到远程云资源或附近的小云。这是缓解资源受限的移动设备和资源匮乏的移动应用程序之间斗争的一种很有前途的方法。由于位置频繁变化，移动用户经常看到动态变化的网络条件，这对感知的应用程序性能有很大影响。因此，在移动环境中很难在运行时做出高质量的卸载决策。为了平衡基于不同卸载决策标准（例如，最小响应时间或能耗）的能量延迟权衡，提出了一种基于Lyapunov优化的节能卸载决策算法。该算法确定何时在本地运行应用程序，何时直接将其转发到云基础设施以进行远程执行，何时通过附近的小云将其委托给云。该算法能够最小化移动设备上的平均能耗，同时确保平均响应时间满足给定的时间约束。此外，与本地和远程执行相比，基于李雅普诺夫的算法可以显着降低能耗，同时只牺牲一小部分响应时间。此外，与基于拉格朗日松弛的聚合成本（基于 LARAC）算法相比，它可以更好地优化能量并且计算复杂度更低。该算法能够最小化移动设备上的平均能耗，同时确保平均响应时间满足给定的时间约束。此外，与本地和远程执行相比，基于李雅普诺夫的算法可以显着降低能耗，同时只牺牲一小部分响应时间。此外，与基于拉格朗日松弛的聚合成本（基于 LARAC）算法相比，它可以更好地优化能量并且计算复杂度更低。该算法能够最小化移动设备上的平均能耗，同时确保平均响应时间满足给定的时间约束。此外，与本地和远程执行相比，基于李雅普诺夫的算法可以显着降低能耗，同时只牺牲一小部分响应时间。此外，与基于拉格朗日松弛的聚合成本（基于 LARAC）算法相比，它可以更好地优化能量并且计算复杂度更低。