A learning automata based approach for module placement in fog computing environment

Abstract

Today, fog computing is an emerging technology to support resource-constrained Internet of Things (IoT) applications. The scalability, geographic distribution, and heterogeneity of edge computing nodes, as well as the diversity of users' expectations, have made optimal module placement, considering the maximum use of fog resources, a challenging optimization problem. This paper proposes a method based on distributed learning automata to reduce the search space by using the maximum capacity of fogs in a heterogeneous fog network. In this method, fog topology is mapped to a distributed learning automata. With the cooperation of the automaton in this DLA, the problem of module placement has been solved to reduce energy consumption and delay of applications. To evaluate the amount of energy consumption and the execution time of IoT applications, two single-objective cost functions for energy and delay and another single-objective function with simultaneous consideration of energy and delay have been used. The results indicate that the average efficiency of the proposed method is 15.99%, 18.21%, and 15.53%, respectively, compared to other methods.