A NOVEL FUZZY RULE BASED DECISION-MAKING APPROACH FOR TASK OFFLOADING ON MULTI-TIER MOBILE-EDGE CLOUD COMPUTING SYSTEMS

Mobile Edge Cloud Computing (MECC) is a developing distributed processing method, which delivers access to CC services at the network’s edge and closer mobile operators. In a speedily varying and dynamic environment, it is very cruel to discover the optimum target server for managing unloaded tasks since we don’t recognize the end user’s demand further. By offloading tasks at the network's edge rather than transmitting them to a remote cloud, MECC can recognize flexibility and actual handling. In the present study, the varying desires of Internet of Things (IoT) applications at dissimilar phases are often neglected in the context of computation offloading. This study introduces fuzzy rule-based decision-making for task offloading approach (FRBDM-TOLA) technique on multi-tier MECC systems. Initially, the presented FRBDM-TOLA technique uses fuzzy clustering algorithms like Fuzzy C-Means (FCM) to categorize tasks into fuzzy subcategories based on their attributes, intensity levels, and temporal aspects. Moreover, the developed method employs the Hybrid Fuzzy-Neural Network (HFNN) approach to select the most suitable target node for the offloading of tasks depending on latency sensitivity function, server capacity, and the state of the network. The HFNN is a hybrid of the FL and the NN used for the rule generation with reference to the intensity level and traffic flow. FL can claim linguistic comments and uncertainties and neural networks can take up complex patterns from the data offered to them. With the aim to improve the performance of the HFNN method the FRBDM-TOLA technique employs Spotted Hyena Optimizer (SHO) for hyperparameter optimization. The developed models optimally employ processes of classification and clustering so as to increase classification accuracy of evaluating network traffic data.