Meta-heuristic Based Hybrid Service Placement Strategies for Two-Level Fog Computing Architecture

The smart manufacturing industry (Industry 4.0) uses the Internet of Things (IoT) devices referred to as Industrial IoT (IIoT) to automate the industrial environment. These IIoT devices generate a massive amount of data called big data. Using fog computing architecture for processing this extensive data will reduce the service time and the service cost for the IIoT applications. The primary challenge is to design better service placement strategies to deploy the IIoT service requests on the fog nodes to minimize service costs and ensure the Quality of Service (QoS) of IIoT applications. Hence, the placement of IIoT services on the fog nodes can be considered as NP-hard problem. In this work, the meta-heuristic-based hybrid algorithms, namely: MGAPSO and EGAPSO, are developed by combining the GA & PSO and Elitism-based GA (EGA) & PSO, respectively. Further, carried out experiments on the two-level fog computing framework developed using docker and containers on 1.4 GHz, 64-bit quad-core processor devices. Experimental results demonstrate that the proposed hybrid EGAPSO algorithm minimizes service time, service cost, and energy consumption and ensures the IIoT applications’ QoS compared to other proposed and state-of-the-art service placement strategies considered for the performance evaluation.