A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

Abstract

In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user's requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double-Q-learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods.