In the IoT-based systems, the fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to reduce the response delay. However, achieving such a benefit is still challenging in the systems with high rate of requests, which imply long queues of tasks in the fog nodes, thus exposing probably an inefficiency in terms of latency to offload the tasks. In addition, a complicated heterogeneous degree in the fog environment introduces an additional issue that many of single fogs can not process heavy tasks due to lack of available resources or limited computing capabilities. To cope with the situation, this article introduces FRATO (Fog Resource aware Adaptive Task Offloading) - a framework for the IoT-fog-cloud systems to offer the minimal service provisioning delay through an adaptive task offloading mechanism. Fundamentally, FRATO is based on the fog resource to select flexibly the optimal offloading policy, which in particular includes a collaborative task offloading solution based on the data fragment concept. In addition, two distributed fog resource allocation algorithms, namely TPRA and MaxRU are developed to deploy the optimized offloading solutions efficiently in cases of resource competition. Through the extensive simulation analysis, the FRATO-based service provisioning approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.

中文翻译：

---

FRATO：基于雾资源的自适应任务卸载，可最大程度地延迟物联网服务供应

在基于IoT的系统中，雾计算允许雾节点以分布式方式（而不是集中式云服务器）以分布式方式卸载和处理从启用IoT的设备请求的任务，以减少响应延迟。但是，在具有高请求率的系统中实现这样的好处仍然是一个挑战，这意味着雾节点中的任务排队很长，因此可能会降低延迟时间以减轻任务负担。此外，雾环境中复杂的异构程度还带来了另一个问题，即由于缺少可用资源或有限的计算能力，许多单个雾无法处理繁重的任务。为了应付这种情况，本文介绍了FRATO（雾资源感知的自适应任务卸载）-IoT-雾-云系统的框架，可通过自适应任务卸载机制提供最小的服务供应延迟。从根本上说，FRATO基于雾资源来灵活选择最佳卸载策略，该策略尤其包括基于数据片段概念的协作任务卸载解决方案。另外，开发了两种分布式雾资源分配算法，即TPRA和MaxRU，以在资源竞争的情况下有效地部署优化的卸载解决方案。通过广泛的仿真分析，