An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

Abstract

In a hierarchical edge‐fog‐cloud architecture, edge devices possess limited resources and energy. To contain with, it can offload some tasks generated by the Internet of Things (IoT) to the fog and cloud. Several factors influence this task‐offloading decision, including hardware features, network conditions, and application characteristics. Most of the research studies, in task offloading systems, have confined to changing parameter values, whereas very few have considered fuzzy‐based dynamic approaches for resource allocation. This work proposes a fuzzy‐based task offloading technique (FBOT) for scheduling the tasks (both compute and data‐intensive) to the appropriate nodes of the fog‐cloud system. The proposed method incorporates a vague logic‐based fuzzy task scheduler for scheduling various tasks at the fog layer. This helps to reduce the waiting time for the compute‐intensive tasks and minimizes the starvation problem of data‐intensive tasks. In addition, the proposed technique decreases the task completion time and selects the best computing nodes for each tasks. To validate the performance of the proposed technique, extensive simulation studies have been carried out on various parameters. Compared with the baseline algorithms, the results show that the proposed method offers an average improvement of 30% in terms of the total time of the task.

Edge Offloading in Smart Grid

“…This will help to deliver real-time energy services at the edge of the smart grid. The dynamic nature of resources in IoT computing farms needs a more robust control mechanism to ensure efficient operation [79]. The offloading architecture aims to minimize the delay while considering energy consumption limitations, and algorithms have been suggested to optimize the delay [80].…”

“…Computational Requirements CPU, Memory, HDD, Devices, Processing Capabilities Processing node selection [75], models based on task nature [77], C-RAN architecture, adaptive algorithms, edge devices offloading [78], control mechanisms [79], offloading architecture [80], computation offloading in cloud robotics [81] Influencing feasibility and efficiency of offloading tasks, dynamic adjustment, and trade-offs between local execution and cloud offloading [80].…”

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

“…Request ET (R ET ) is the time involved between R est and request execution finish time ( R eft ) , which is calculated as R ET = R eft − R est . The average ET (A ET ), of a given batch of RT and NRT requests, is computed using Equation (2).…”

“…However, it has been observed that the Cloud-based services may require more time to respond which sometimes are not feasible for the requests requiring ultra-low latency such as RT/IoT requests. 2 Fog computing addresses these challenges well and fills the gap between Cloud and IoT applications by enabling computing, storage, and networking infrastructure near the IoT devices. 3 In a three-tier architecture, Fog computing lies between Cloud and IoT/edge devices.…”