Abstract
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.
Highlights
Fifth-generation (5G) cellular technologies have enabled various new applications. such as video streaming analysis, augmented reality (AR), the Internet of Things (IoT), and autonomous driving [1]
Mobile users can access cloud computing over a wide area network (WAN) to process elastic services and conduct data-intensive analysis
Based on the wireless local area network (WLAN), the metropolitan area network (MAN), and WAN communications, and the requirements of the application task, the decision can be made based on the edge/fog computing infrastructure [14]
Summary
Fifth-generation (5G) cellular technologies have enabled various new applications. such as video streaming analysis, augmented reality (AR), the Internet of Things (IoT), and autonomous driving [1]. Based on the WLAN, the metropolitan area network (MAN), and WAN communications, and the requirements of the application task, the decision can be made based on the edge/fog computing infrastructure [14]. The role of a fuzzy-based MEO is to find a target server that can be a local edge server, a neighboring edge server, or a cloud server based on the profile of an incoming application task and mobile edge computing characteristics. This system did not study the packet success ratio in the WLAN environment and the resource capability of a mobile device. Bittencourt et al [14] proposed the edge-ward placement algorithm, where modules of the
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