Optimizing IoT Workloads for Fog and Edge Scheduling Algorithms: A Comparative Study

The rapid growth of Internet of Things (IoT) applications has led to increased interest in leveraging fog and edge computing for efficient resource management and task scheduling. In this article, we present a comparative analysis of scheduling algorithms for fog and edge computing in the context of IoT. We start by providing an overview of fog and edge computing, highlighting their significance in supporting IoT applications. We then review and analyze a variety of scheduling algorithms proposed for fog and edge computing environments, considering factors such as task allocation, load balancing, energy efficiency, latency, and scalability. Through this analysis, we identify the strengths and weaknesses of different scheduling algorithms and highlight their suitability for various IoT application scenarios. Furthermore, we discuss the trade-offs associated with different algorithmic approaches and provide insights into future research directions. The findings of this comparative analysis serve as a valuable reference for researchers and practitioners in selecting and designing scheduling algorithms that can optimize the performance of fog and edge computing systems in IoT environments.