QoS Performance Enhancement Policy through Combining Fog and SDN

• Published in: Simulation modelling practice and theory 2021-05, Vol.109, p.102292, Article 102292
• Main Authors: Ahammad, Ishtiaq, Khan, Md. Ashikur Rahman, Salehin, Zayed Us
• Format: Article
• Language: English
• Subjects: Fog Computing; iFogSim Simulator; Internet of Things; Modeling and Simulation; Quality of Service; Software-Defined Networking
• ISSN: 1569-190X; 1878-1462
• DOI: 10.1016/j.simpat.2021.102292

The goal of Internet of Things (IoT) is to bring any object online, thereby creating a massive volume of data that can overwhelm the existing computing and networking technologies. Therefore, centered cloud isn't ideal for rapidly expanding IoT environmental requirements. Fog computing (FC) moves some portion of the computing load (related to real-time services) from the cloud into edge fog devices. FC is expected to become the subsequent major computing transition and this one has ability to overcome existing cloud limitations. However the key obstacles facing FC are: wide distribution, isolated coupling, quality-of-service (QoS) regulation, adaptability to conditions, and particularly the standardization and normalization is still in phase of development. Software defined networking (SDN) will help fog to solve these obstacles. SDN means unified network control plane (which is separated from data plane), allowing the introduction for advanced traffic control and the orchestration mechanisms of networks and resources. On the grounds of SDN concept, and then combining it with FC, the network type can be modified to resolve all those cloud drawbacks and improve IoT system's QoS. Within this paper, architecture is developed through the combination of independently researched areas of SDN and FC to enhance the QoS in an IoT system. An algorithm (which is dependent on partition the SDN virtually) is presented to support the architecture whose purpose is to select the optimal access point and optimal place to process the data. The main objective of this algorithm is to provide improved QoS by partitioning the corresponding fog devices through the SDN controller. A use case dependent on the presented architecture and algorithm is then provided and assessed this use case's QoS parameter values (network usage, cost, latency and power consumption) using the iFogSim simulator. In contrast to cloud-only deployment, the result indicates a major enhancement of the mentioned QoS parameter values in the deployment of fog with SDN. In addition, once compared to a relative former identical use case; the findings of this paper show improved results for power consumption, network usage and latency. In fact, when compared to a former identical use case, the outcome of this paper shows around 3 times less latency and 2 times less network usage. Finally the ground (IoMT, Industry 4.0, Green IoT, and 5G) that is influenced by this QoS improvement is broadly illustrated in this p