With the explosive growth of the Internet of Things (IoT), fog computing emerged as a new paradigm, in an attempt to minimize network latency. Fog computing extends the cloud to the network edge, closer to where the IoT data are generated. Typically, fog resources are of limited capacity. On the other hand, IoT applications are becoming more and more complex and computationally demanding, requiring a certain level of Quality of Service (QoS) within strict time constraints. In such a real-time setting, it is often more desirable for a job to meet its deadline by producing an approximate—but still of acceptable quality—result, rather than producing an overdue precise result. Based on this concept, in this paper we examine the orchestration of real-time IoT workflows in a heterogeneous fog computing environment, utilizing partial computations. When a workflow task produces an imprecise result, the error may be propagated not only to its immediate child tasks, but also across subsequent successor tasks of the workflow, ultimately affecting its end-result. The proposed scheduling technique is compared to a baseline algorithm, where partial computations are not used, under various result precision thresholds and input error propagation probabilities. The simulation results reveal that the proposed heuristic can provide on average a 32.71% lower deadline miss ratio than the baseline policy, by trading off an average result precision of 2.43%.

随着物联网 (IoT) 的爆炸式增长，雾计算作为一种新范式出现，试图最大限度地减少网络延迟。雾计算将云扩展到网络边缘，更靠近物联网数据的生成位置。通常，雾资源的容量有限。另一方面，物联网应用变得越来越复杂，对计算的要求越来越高，需要在严格的时间限制内达到一定水平的服务质量 (QoS)。在这种实时设置中，通常更希望作业通过产生近似但仍具有可接受质量的结果来满足其最后期限，而不是产生逾期的精确结果。基于这一概念，在本文中，我们利用部分计算研究了异构雾计算环境中实时物联网工作流的编排。当工作流任务产生不精确的结果时，错误可能不仅会传播到其直接子任务，还会传播到工作流的后续后续任务，最终影响其最终结果。在各种结果精度阈值和输入错误传播概率下，将所提出的调度技术与不使用部分计算的基线算法进行比较。模拟结果表明，通过权衡 2.43% 的平均结果精度，所提出的启发式算法可以提供比基线策略平均低 32.71% 的截止日期未命中率。在各种结果精度阈值和输入错误传播概率下，将所提出的调度技术与不使用部分计算的基线算法进行比较。模拟结果表明，通过权衡 2.43% 的平均结果精度，所提出的启发式算法可以提供比基线策略平均低 32.71% 的截止日期未命中率。在各种结果精度阈值和输入错误传播概率下，将所提出的调度技术与不使用部分计算的基线算法进行比较。模拟结果表明，通过权衡 2.43% 的平均结果精度，所提出的启发式算法可以提供比基线策略平均低 32.71% 的截止日期未命中率。