Efficient Resource Allocation in Fog Computing Systems for Internet of Things Applications

Thesis Abstract

Abstract
Fog computing has emerged as a promising paradigm to support Internet of Things (IoT) applications by providing computational resources closer to the edge of the network. Efficient resource allocation in fog computing systems is crucial for optimizing performance and meeting the diverse requirements of IoT applications. This thesis investigates the challenges and opportunities in resource allocation within fog computing systems to enhance the overall performance and scalability of IoT applications. The research begins with a comprehensive review of the background of fog computing and IoT, highlighting the growing importance of efficient resource allocation in this context. The problem statement identifies the key issues related to resource allocation in fog computing systems, such as dynamic workload variations, limited resources, and heterogeneous devices. The objectives of the study aim to propose a novel resource allocation framework that can adapt to changing IoT application demands while maximizing resource utilization and minimizing latency. The study also discusses the limitations and scope of the research, acknowledging constraints such as the complexity of real-world IoT environments and the need for practical implementation considerations. The significance of the study lies in its potential to contribute to the advancement of fog computing technologies and improve the overall performance of IoT applications in various domains. The structure of the thesis is organized into five main chapters. Chapter 1 provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, and definition of key terms. Chapter 2 presents a detailed literature review on resource allocation techniques in fog computing systems, highlighting existing approaches, their limitations, and potential areas for improvement. Chapter 3 outlines the research methodology, including the design of the resource allocation framework, simulation setup, evaluation metrics, and data collection methods. The chapter also discusses the experimental setup and data analysis techniques used to validate the proposed framework. Chapter 4 presents a comprehensive discussion of the research findings, including the performance evaluation of the proposed resource allocation framework in various IoT scenarios. The results are analyzed in detail to demonstrate the effectiveness of the framework in optimizing resource allocation and improving the overall performance of IoT applications. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further research in the field of resource allocation in fog computing systems for IoT applications. Overall, this thesis contributes to the advancement of fog computing technologies and provides valuable insights into the optimization of resource allocation for efficient IoT application deployment.