Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.

中文翻译：

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基于随机化的移动雾计算动态规划卸载算法

卸载到雾服务器使得在本地设备中处理繁重的计算负载任务成为可能。然而，由于卸载决策的生成问题是一个 NP 问题，它无法最优或传统地解决，尤其是在多任务卸载场景中。因此，本文基于遗传优化理论，提出了一种基于随机化的动态规划卸载算法，以解决移动雾计算中的卸载决策生成问题。该算法创新性地设计了动态规划填表方法，即迭代生成一组随机卸载决策。如果这些集合中的一些改进了 DP 表中的决策，那么它们将被合并到表中。迭代DP表还用于改进迭代中生成的决策集，以获得最优卸载近似解。大量模拟表明，所提出的 DPOA 可以在 3 ms 内生成决策，并且当用户处于多任务卸载场景时，这一优势尤为显着。