الفهرس 
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Abstract
Recently, there is a wide use of cloud computing in many applications and services. However, some applications and services require specified requirements which lead to the utilization of fog computing. Fog computing is an extension of it to utilize resources from devices close to the edge. Fog computing in some applications like healthcare system applications needs a standardization and a suitable real-time scheduling algorithm. The goal of this thesis is to design a new proposed LB strategy named Load Balancing and Optimization Strategy (LBOS). LBOS uses a dynamic resource allocation method based on Reinforcement learning and genetic algorithm is proposed. LBOS monitors the traffic in the network continuously, collects the information about each server load, handles the incoming requests, and distributes them between the available servers equally using dynamic resource allocation method. Hence, it enhances the performance even when it’s the peak time. Accordingly, LBOS is simple and efficient in real-time systems in fog computing such as in the case of healthcare system. LBOS is concerned with designing an IoT-Fog based Healthcare System. The proposed IoT-Fog system consists of three layers, namely: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. Finally, the experiments are carried out and the results show that the proposed solution improves the Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost and reduce the response time. Comparing the LBOS with the state-of-the-art algorithms, it achieved the best Load Balancing Level (85.71%). Hence, LBOS is an efficient way to establish the resource utilization and ensure the continuous service.