An Autonomous Planning Model for Solving IoT Service Placement Problem Using the Imperialist Competitive Algorithm

The growth of the Internet of Things (IoT) can lead to improved productivity, scalability, connectivity, and saving time and money. However, the increasing number of IoT-based applications has MAPE the centralized cloud computing paradigm face several challenges such as latency and bandwidth. Nowadays, fog computing with distributed architecture has emerged to support latency-sensitive IoT applications with limited resources. Using the fog instead of the cloud can bring the storage and computing facilities closer to the edge of the network and thus provide better performance for the end user. Meanwhile, IoT applications usually have complex multi-component structures whose efficient placement on fog nodes can overcome the resource restrictions of IoT devices. This problem as the IoT Service Placement Problem (SPP) is NP-hard, where nature-inspired approaches widely provide robust solutions to solve it. In this paper, a meta-heuristic-based evolutionary approach named SPP-ICA is presented to address SPP, which originates from the Imperialist Competitive Algorithm (ICA). SPP-ICA is developed based on MAPE-K autonomous planning model, so that it applies service deployment priority and resource consumption distribution in the placement process. ICA embedded in SPP-ICA leads to more effective placement of IoT services in terms of latency and resource utilization by considering the concepts of elitism and balanced resource consumption. Experimental results show that SPP-ICA performs significantly better than state-of-the-art algorithms with meta-heuristic structure. On average, SPP-ICA deploys more IoT services on fog and reduces service latency by 9%.