In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

中文翻译：

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物联网应用中基于模糊的移动边缘协调器中的灵活计算分流

在物联网（IoT）时代，容量受限的Internet和各种新应用（如视频流分析和增强现实）的不可控制的服务延迟是挑战。云计算系统，也称为将IoT应用程序的耗能计算转移到云服务器的解决方案，无法满足对延迟敏感和上下文感知的服务要求。为了解决这个问题，边缘计算系统通过使计算和存储更接近用户来提供及时的上下文感知服务。对于边缘和云计算系统而言，可以有效处理的动态请求流是一项重大挑战。为了提高IoT系统的性能，移动边缘协调器（MEO）是一个应用程序放置控制器，是通过将最终移动设备与边缘和云计算系统集成而设计的。本文针对物联网应用提出了一种基于模糊MEO的灵活计算分流方法，以提高计算资源管理的效率。考虑到网络，计算资源和任务要求，基于模糊的MEO允许边缘工作流程协调操作来决定是否将移动用户卸载到本地边缘，相邻边缘或云服务器。此外，当移动设备数量增加时，增加的数据包大小将影响失败任务比率。为了减少由于传输冲突而导致的失败任务并提高对时间要求严格的任务的服务时间，我们为基于模糊的MEO定义了新的输入明晰值和新的输出决策。使用EdgeCloudSim模拟器，我们在增强现实，医疗保健，计算密集型和信息娱乐应用中使用四种基准算法评估了我们的建议。仿真结果表明，我们的建议在WLAN延迟，服务时间，失败的任务数量和VM利用率方面提供了更好的结果。