This book chapter focuses on the analysis of quality of service (QoS) and performance modelling in the context of fog computing. The chapter proposes analytical frameworks for QoS analysis and performance modelling of fog computing systems. The chapter starts with an introduction to fog computing and the importance of QoS analysis in such systems. The next section presents a literature review of related work and different approaches to QoS analysis in fog computing. The proposed analytical frameworks are then described in detail, including their different components and assumptions. Case studies are also presented to demonstrate the application of the analytical frameworks. The case studies include a scenario of a fog computing system with a specific architecture and different performance metrics and models used for the analysis. The results and analysis of the case studies are then presented. Finally, the chapter concludes with a discussion of the key findings and contributions of the analytical frameworks.
Top1. Introduction
Fog computing is a distributed computing paradigm that extends cloud computing capabilities closer to the edge of the network (Das & Inuwa, 2023). It aims to address the limitations of traditional cloud computing in terms of latency, bandwidth constraints, and real-time data processing requirements (Hazra et al., 2023). Fog computing leverages resources available at the edge of the network, such as edge devices, gateways, and edge servers, to enable faster and more efficient processing of data and services (Saad, 2018). In fog computing, data is processed and analysed at the network edge, in close proximity to the devices generating the data. This proximity allows for reduced latency and improved response times, making fog computing suitable for applications that require real-time or near-real-time processing (Das & Inuwa, 2023). By offloading computation and storage tasks from the cloud to the edge, fog computing also helps alleviate network congestion and reduces the amount of data that needs to be transmitted to the cloud (Songhorabadi et al., 2023).
Quality of Service (QoS) refers to the performance characteristics of a system or service that determine its ability to meet specific requirements and expectations of users. In the context of fog computing, QoS analysis involves evaluating and assessing the performance of fog computing systems to ensure that they meet the desired service levels and performance objectives (Liu et al., 2017), (Hussein et al., 2023). Therefore, QoS analysis in fog computing is crucial due to the distributed nature of the infrastructure and the need for efficient modeling and management. It involves measuring and analysing various performance metrics, such as latency, throughput, reliability, availability, and scalability (Umoh et al., 2023). QoS analysis helps in understanding the behavior and performance of fog computing systems, identifying potential bottlenecks or areas for improvement, and making informed decisions for resource allocation, task scheduling, and service provisioning (Shafik et al., 2019), (Sensi et al., 2022).