Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

Abstract

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan.