Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

中文翻译：

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边缘云环境中用于IoT任务卸载的新颖方法

最近，连接到Internet的物联网（IoT）设备的数量以及这些设备产生的数据急剧增加。这将需要分担IoT任务，以将大量的计算和存储释放到资源丰富的节点（如边缘计算和云计算）。尽管边缘计算是解决延迟敏感相关问题的有前途的推动者，但其部署却带来了新的挑战。此外，不同的服务架构和卸载策略对物联网应用的服务时间性能也会产生不同的影响。因此，本文提出了一种用于Edge-Cloud系统中任务卸载的新颖方法，以最大程度地减少对延迟敏感的应用程序的总体服务时间。考虑到应用程序的特性（例如，CPU需求，网络需求和延迟敏感性）以及资源利用率和资源异构性。进行了许多仿真实验，以与其他相关方法一起评估所提出的方法，发现该方法可以改善对延迟敏感的应用程序的总体服务时间，并有效地利用边缘云资源。此外，结果表明，由于计算资源和通信类型的原因，Edge-Cloud系统中的不同卸载决策可能导致各种服务时间。发现它可以缩短对延迟敏感的应用程序的总体服务时间，并有效地利用边缘云资源。此外，结果表明，由于计算资源和通信类型的原因，Edge-Cloud系统中的不同卸载决策可能导致各种服务时间。发现它可以缩短对延迟敏感的应用程序的总体服务时间，并有效地利用边缘云资源。此外，结果表明，由于计算资源和通信类型的原因，Edge-Cloud系统中的不同卸载决策可能导致各种服务时间。