Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach. (3rd June 2022)

• Record Type: Journal Article
• Authors: Ghobaei‐Arani, Mostafa; Asadianfam, Shiva; Abolfathi, Ahad
• Is Part Of: Software, practice & experience. Volume 52:Number 9(2022)
• Page Start: 1967
• Page End: 1986
• Publication Date: 2022-06-03
• Subjects: IoT -- fog computing -- graph partitioning -- service placement.; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3111
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud computing. This study proposed a new approach to service placement generated by running applications on IoT devices in the fog computing. IoT devices send applications to the fog environment that each application contains a set of services. The purpose of solving the IoT services placement problem is to efficiently deploy these services on fog cells. For this purpose, it is assumed that the received services from the IoT applications are received as a directed acyclic graph that depicts the communication between the cells within the graph that shows the communication between the services. Then, the imperialist competitive algorithm is used to place and select the destination for IoT services. The simulation results of the iFogSim simulator in different experiments showed that the imperialist competitive algorithm with the proposed graph partitioning approach has improved service placement on the fog infrastructure compared to the genetic algorithm and best‐fit algorithm.