Fog-Based Resource Allocation Hybrid Approach Using Metaheuristic for Mobile Networks

In recent years, the increasing use of the mobile-fog- server application has generated excessive amounts of data with different data types such as images, documents, multimedia and other files. It is difficult to manage and control the volume of data used in fog computing, and since fog-server computing has problems with latency, resource allocation, and fitness. To overcome these problems, fog-server computing has been used; it consists of centralized fog node, five servers and a set of mobile nodes for each case study. The proposed method is applied in fog node with three modules. The first module is Meta-heuristic Particle Swarm Optimization (PSO) to enhance the effectiveness, better search speed, and improve Cost efficiency for mobile devices. The second module is Harris hawks optimization (HHO) to maintain the quality of the data collected in mobile network. The third module is hybrid resource allocation algorithm based on integrated two optimization methods are HHO and PSO to build a value Better Fitness Load Balancing (BFLB) generated from the both optimizers based on their characteristics. Based on simulation results and comparison of Hybrid resource allocation with traditional and related works, PSO meta-heuristic, and HHO algorithms, the proposed Hybrid system is better with average makespan is 11.7614 Seconds, average execution cost is 122.338 Seconds, and average processing time is 14.282 seconds.