Security-aware resource allocation in fog computing using a meta-heuristic algorithm

Security-aware resource allocation in fog computing using a meta-heuristic algorithm

연구 분야: Infrastructure

학회: Cluster Computing

In recent years, the number of smart devices and wireless data transmissions has increased worldwide. These emerging applications and services require not only extensive computing capabilities and high battery power, but also elevated data transmissions as well. Nonetheless, the computing capacity of this equipment is constrained, resulting in significant consequences on the performance and operating costs of services in 5th-generation wireless networks. Recent advantages of Fog computing have increased the use of this model to fulfill above requirements in the IoT context. A new Fog computing network model has been proposed in order to address these issues by providing cloud computing services at the network’s edge. In Fog computing, mobile devices are not required to offload all their tasks to remote and central servers. However, since other users are exposed to offloaded tasks, they are vulnerable to malicious attacks and eavesdropping. In this paper, we investigate security-aware resource allocation in device-to-device based fog computing systems. In order to enhance task offloading, a novel multi-objective function is proposed to optimize delay and energy savings compared to local computing, as well as security breach costs. Several multi-objective meta-heuristic algorithms, such as Non-dominated Sorting Genetic Algorithm II (NSGA-II), have been proposed over the past decade. Optimizing objectives, such as energy consumption and delay was the goal of using these algorithms. Subsequently, the improved NSGA-II algorithm is employed to solve the problem. In this algorithm, the Sigma Scaling, a technique used to adjust fitness values to maintain diversity in the population, is utilized to control selection pressure. By incorporating Sigma Scaling, the exploration and exploitation capabilities of the algorithm are effectively managed, enhancing its ability to escape local optima and prevent premature convergence. The performance of our modified NSGA-II is superior to that of both the standard version and other state-of-the-art methods. Simulation results indicate that the proposed algorithm, despite its simplicity in implementation, achieves a 30.15% improvement in performance compared to the NSGA-II. These findings show that our approach is effective in achieving multiple objectives in fog computing environments, making it a significant advancement over current techniques.