A STRUCTURED TWO-PHASE MODEL FOR EFFICIENT IOT-FOGCLOUD RESOURCE ALLOCATION

The amount of data produced by intelligent devices has grown significantly with the introduction of the Internet of Things (IoT) concept and the rapid advancement of technology. To analyze and store the created data, cloud computing offers limitless processing and storage capacity. Nevertheless, the cloud computing paradigm is linked to a lack of geographical awareness, excessive energy consumption, and significant transmission delay. However, the data produced by the intelligent devices must be analysed instantly because it is delay-sensitive. Cloud computing is therefore unsuitable for handling this delay-sensitive data. The fog paradigm, which enables data processing close to IoT devices, was proposed to mitigate the problems with the cloud paradigm. The fog paradigm's constraints, which render it unsuited for processing massive amounts of data, are one of its common characteristics. The fog paradigm must work with the cloud paradigm to accomplish a shared objective in order to guarantee the efficient completion of operations involving delay-sensitive applications and the massive amount of data created. In order to effectively and efficiently employ the fog and cloud resources for carrying out operations that are sensitive to delays and the massive amount of data produced by end users, an efficient resource allocation system is put forth in this study. There are two steps involved in assigning resources to assignments. Prior to being assigned to appropriate resources in the layers of their respective classes, the tasks in the arrival queue are first categorized according to the task guarantee ratio on the cloud and fog layers. Second, in order to categorize freshly arrived tasks and assign appropriate resources to the tasks for execution in the layers of their respective classes, we apply Bayes' classifier using prior allocation history data. The system's execution time and latency are decreased by creating an ideal resource allocation in the fog and cloud layers using a Crayfish Optimization Algorithm (COA). The iFogSim simulator toolkit is used to construct the suggested method, and the execution results show greater promise than the state-of-the-art techniques.