Ibrahim Kashif, Sajid Ahthasham, Ullah Ihsan, Ullah Khan Inam, Kaushik Keshav, Askar S S, Abouhawwash Mohamed
Department of Computer Science and Information Technology, University of Balochistan, Quetta, Balochistan, Pakistan.
Department of Cyber Security and Data Science, Riphah Institute of Systems Engineering, Riphah International University, Islamabad, Pakistan.
PeerJ Comput Sci. 2025 Mar 28;11:e2657. doi: 10.7717/peerj-cs.2657. eCollection 2025.
The key objective of edge computing is to reduce delays and provide consumers with high-quality services. However, there are certain challenges, such as high user mobility and the dynamic environments created by IoT devices. Additionally, the limitations of constrained device resources impede effective task completion. The challenge of task offloading plays a crucial role as one of the key challenges for edge computing, which is addressed in this research. An efficient rule-based task-offloading model (FI-RBTOM) is proposed in this context. The key decision of the proposed model is to choose either the task to be offloaded over an edge server or the cloud server or it can be processed over a local node. The four important input parameters are bandwidth, CPU utilization, task length, and task size. The proposed (FI-RBTOM), simulation is carried out using MATLAB (fuzzy logic) tool with 75% training and 25% testing with an overall error rate of 0.39875 is achieved.
边缘计算的关键目标是减少延迟并为用户提供高质量服务。然而，存在一些挑战，例如用户移动性高以及物联网设备所创造的动态环境。此外，受限设备资源的限制阻碍了有效任务的完成。任务卸载的挑战作为边缘计算的关键挑战之一起着至关重要的作用，本研究对此进行了探讨。在此背景下提出了一种基于规则的高效任务卸载模型（FI-RBTOM）。该模型的关键决策是选择在边缘服务器或云服务器上卸载任务，或者在本地节点上进行处理。四个重要的输入参数是带宽、CPU利用率、任务长度和任务大小。使用MATLAB（模糊逻辑）工具对所提出的（FI-RBTOM）进行了仿真，训练占75%，测试占25%，总体错误率达到0.39875。
