Cluster computing, grid computing, and cloud computing represent three distinct paradigms in distributed computing, each with unique characteristics, physical structures, hardware requirements, resource management strategies, applications, networking approaches, and scalability features. Understanding their differences is crucial, especially in the context of integrating Internet of Things (IoT) devices, where data volumes, real-time processing, and resource management are critical considerations. Cluster computing involves a tightly coupled set of computers, often located in the same physical location, that work together as a single system. Its key characteristic is high performance computing (HPC) through parallelism, where multiple nodes share a task by breaking it down into smaller sub-tasks. The physical structure of a cluster is centralized, with all nodes typically connected via a high-speed local area network (LAN) within a single data center or server room. Hardware in clusters is relatively homogeneous, consisting of similar processors, memory capacities, and storage configurations, ensuring predictable performance. Resources in cluster computing are dedicated to the cluster’s tasks, and resource management is centrally controlled through job schedulers such as SLURM or PBS. Applications for clusters often include scientific simulations, data analysis, and modeling, where computational power and low latency are critical. Networking within a cluster is designed for high throughput and low latency, usually via Gigabit Ethernet or InfiniBand, allowing efficient data sharing among nodes. Scalability in cluster computing is somewhat limited, as adding new nodes requires careful integration to maintain system balance, and scaling beyond a certain point increases network and management complexity. Grid computing, in contrast, is loosely coupled and designed to harness geographically distributed resources across multiple administrative domains. Its defining characteristic is resource sharing across different organizations, allowing users to utilize idle computing power over a wide area. The physical structure of grid computing is decentralized, often spanning multiple institutions or even countries, connected over a wide area network (WAN). The hardware in a grid is heterogeneous, including different types of processors, storage systems, and operating systems, which requires middleware to manage the interoperability of resources. Resources in grid computing are often non-dedicated; they may become available or unavailable depending on local policies, requiring dynamic scheduling and fault-tolerant mechanisms. Grid applications include large-scale scientific computations, research collaborations, and projects like SETI@home, which require massive computational power but not necessarily low latency. Networking in grids relies on standard internet protocols and virtual private networks (VPNs) to handle secure communication over long distances, which introduces higher latency compared to clusters. Scalability in grid computing is inherently flexible, as additional nodes or sites can be incorporated easily, but performance may be limited by network latency and the heterogeneity of resources. Cloud computing