Distributed IoT services placement in fog environment using optimization-based evolutionary approaches

Fog computing is a promising computing paradigm for the processing and storage of massive data generated by Internet of Things (IoT) devices. Fog computing devices have limited resources compared to the cloud. Meanwhile, the dynamism and heterogeneity of applications requested from IoT devices require quick decisions on where to process services on the fog, the cloud, or the hybrid. This problem is known as Fog Service Placement (FSP), which is a computationally NP-hard problem. Therefore, the efficient allocation of fog resources to IoT services by non-deterministic algorithms is emphasized. In this paper, a Quality of Service (QoS)-aware deployment scheme is proposed using an evolutionary approach to address the FSP problem. Here, several new evolutionary approaches such as Teaching Learning-Based Optimization (TLBO), Honey Badger Algorithm (HBA) and Harris Hawks Optimizer (HHO) are analyzed to solve the problem. We improve the evolutionary approach used to solve the FSP problem with a shared parallel architecture and perform distributed fog resource management. In addition, we bring the processing closer to the fog network by considering the priority of service execution. Here, we formulate a multi-objective function as an optimization problem that simultaneously considers delay cost, delay violation, service cost and fog utilization. All the parameters needed to configure the objective function are collected based on the incoming traffic. The simulations show the superiority of HBA for solving the FSP problem. The evaluation results show that the proposed placement scheme improves the cost and delay compared to the best existing method by 2.6% and 2.8%, respectively.