Application offloading to clouds is the key enabler for compute-intensive applications running on mobile devices. An offloading algorithm employs estimated averages of the execution and communication costs of application modules to decide on a modules subset to be offloaded with the objective of minimizing a certain metric (e.g., execution time or energy). This decision is highly affected by the inherent uncertainty arising from the estimated cost averages due to natural fluctuations or measurement inaccuracies. In this article, we propose a novel offloading scheme that takes into consideration these uncertainties. The proposed work first formulates the offloading problem as a tractable robust optimization one where the uncertainty in $k$k cost parameters is incorporated by allowing these parameters to fluctuate within intervals specified from profiling the application and the network. We then show that this problem can be transformed into $k+1$k+1 binary linear programs that are solved while preserving the complexity of the original problem. In contrast to existing approaches, the performance of the obtained decision is guaranteed as long as the behavior of the uncertain parameters remains within the given intervals. Performance evaluation results using a face detection and synthetically generated applications with a large number of modules demonstrate the robustness of the obtained offloading decisions.

中文翻译：

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用于将应用程序高效卸载到云的稳健公式

应用程序卸载到云是在移动设备上运行的计算密集型应用程序的关键推动因素。卸载算法采用应用模块的执行和通信成本的估计平均值来决定要卸载的模块子集，目标是最小化某个度量（例如，执行时间或能量）。由于自然波动或测量不准确，估计成本平均值所产生的固有不确定性对这一决定有很大影响。在本文中，我们提出了一种考虑到这些不确定性的新型卸载方案。所提出的工作首先将卸载问题表述为一种易于处理的鲁棒优化问题，其中不确定性$千$克通过允许这些参数在从分析应用程序和网络而指定的时间间隔内波动来合并成本参数。然后我们证明这个问题可以转化为$k+1$克+1在保留原始问题的复杂性的同时求解的二进制线性程序。与现有方法相比，只要不确定参数的行为保持在给定的区间内，就可以保证所获得决策的性能。使用人脸检测和具有大量模块的综合生成应用程序的性能评估结果证明了所获得的卸载决策的鲁棒性。