Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment

Fog computing is an emerging computation technology for handling and processing the data from IoT devices. The devices such as the router, smart gateways, or micro-data centers are used as the fog nodes to host and service the IoT applications. However, the primary challenge in fog computing is to find the suitable nodes to deploy and run the IoT application services as these devices are geographically distributed and have limited computational resources. In this paper, we design the two-level resource provisioning fog framework using docker and containers and formulate the service placement problem in fog computing environment as a multi-objective optimization problem for minimizing the service time, cost, energy consumption and thus ensuring the QoS of IoT applications. We solved the said multi-objective problem using the Elitism-based Genetic Algorithm (EGA). The proposed approach is evaluated on fog computing testbed developed using docker and containers on 1.4 GHz 64-bit quad-core processor devices. The experimental results demonstrate that the proposed method outperforms other state-of-the-art service placement strategies considered for performance evaluation in terms of service cost, energy consumption, and service time.

Automated summary

The paper proposes a two-level resource provisioning fog framework using docker and containers, formulates the service placement problem in fog computing environment as a multi-objective optimization problem to ensure the QoS of IoT applications, and solves it with the Elitism-based Genetic Algorithm (EGA). The proposed approach outperforms other state-of-the-art service placement strategies in terms of service cost, energy consumption, and service time according to experimental results on a fog computing testbed developed using docker and containers on 1.4 GHz 64-bit quad-core processor devices.