Solving the Multi-Objective Problem of IoT Service Placement in Fog Computing Using Cuckoo Search Algorithm

The Internet of Things (IoT) has led to the proliferation of networked computing devices in the public, commercial, and private sectors. These devices often demand real-time computational resources, which are rather challenging to provide by the conventional cloud computing paradigm. In this regard, the emerging fog computing subtly solves this problem by providing more agile access to local storage and computational resources. Fog computing is a novel computational paradigm that provides resources in the proximity of IoT devices in such a way that all fog cells are located at the edge of the network. Although the theoretical foundations of fog computing have already been established, the problem of services placement for mapping IoT applications to fog cells aiming at maximizing the utilization of fog resources and improving Quality of Service (QoS) is still challenging. In order to fill this gap, we have developed a conceptual computing framework based on combination of cloud-fog by introducing the cloud-fog control middleware that manages service requests to meet some constraints. In this framework, the fog service placement problem is modeled as a multi-objective optimization problem that considers the heterogeneity of resources and applications based on QoS requirements. Finally, we propose an evolutionary algorithm based on the cuckoo search to solve the problem. The simulation results show that the proposed approach provides better performance compared to its counterparts in terms of various metrics such as fog utilization, energy consumption, number of performed services, response time, and service delay.