The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among FNs. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the deep autoencoder (DAE) model, and the FNs are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal FN selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal FN placement problems in efficiently executing dynamic industrial IoT workloads.

中文翻译：

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支持雾的工业物联网的自主工作负载预测和资源分配框架

物联网 (IoT) 以众多设施和进步彻底改变了工业领域。工业物联网系统需要借助雾计算平台执行延迟感知工作负载，并且需要在雾节点 (FN) 中进行精确的资源分配，以最小的成本和延迟执行波动的工业物联网工作负载。鉴于上述问题，我们引入了一个自主的工作负载预测和资源分配框架，可以在 FN 之间有效地分配资源。在所提出的框架中，在深度自动编码器 (DAE) 模型的指导下，在分析阶段预测工作负载，并根据工业物联网工作负载的需求对 FN 进行缩放。乌鸦搜索算法 (CSA) 与最佳 FN 选择框架集成，以改善成本和延迟目标。所提出的方案在执行成本、请求拒绝率、吞吐量和响应时间方面与现有的优化模型进行了评估和比较。仿真结果表明，所提出的方案优于其他优化模型。该方法为有效执行动态工业物联网工作负载的最佳 FN 放置问题提供了合适的解决方案。