Editing Introduction to Edge Computing (section)

== 1.4 Different Edge Computing Paradigms ==
===1.4.1 Fog Computing ===
Fog computing is an architecture introduced by Cisco to extend cloud services to the edge of the network. Instead of sending data from edge devices directly to the cloud, fog computing enables processing, storage, and analysis to occur closer to the data source, such as on local routers, gateways, or switches. This paradigm creates a layered system where intermediate nodes handle computations, reducing latency and bandwidth usage. It supports a decentralized model, which is ideal for real-time applications like industrial automation, smart traffic lights, and autonomous systems. Fog computing enhances scalability and responsiveness while maintaining a link to the central cloud for more intensive processing or long-term storage.
=== 1.4.2 Mobile Edge Computing (MEC) ===
Mobile Edge Computing, now often referred to as Multi-access Edge Computing (MEC), is designed to bring computational capabilities directly into the radio access network (RAN), especially at 4G or 5G base stations. This paradigm enables real-time data processing with ultra-low latency and high bandwidth by placing services physically close to mobile users. MEC systems can take advantage of location and context information from the mobile network, making them ideal for time-sensitive and location-aware applications such as augmented reality (AR), virtual reality (VR), real-time video analytics, and connected vehicle systems. MEC is crucial in enabling the responsiveness and scalability expected from next-generation mobile networks.
===1.4.3 Edge Cloud ===
The edge cloud paradigm combines traditional cloud computing with edge computing in a federated or hybrid architecture. It enables dynamic resource sharing between cloud data centers and edge infrastructure, offering the best of both worlds: centralized power and local responsiveness. Edge cloud systems support elasticity, scalability, and fault tolerance while minimizing latency. This paradigm is especially relevant for large-scale applications that require real-time responsiveness with centralized oversight, such as smart city infrastructure, telemedicine, and distributed AI models. The edge cloud facilitates efficient load balancing, allowing tasks to be executed wherever it’s most optimal based on conditions like network congestion, location, and data urgency.
=== 1.4.4  Cloudlet Computing ===
Cloudlet computing introduces the idea of deploying small-scale cloud data centers known as "cloudlets" at the edge of the network, typically near mobile devices. Originally proposed by Carnegie Mellon University, cloudlets act as intermediate computation nodes that are more powerful than mobile devices but closer than the cloud. They enable mobile applications to offload resource-intensive tasks such as speech recognition, facial recognition, or gaming graphics rendering. Because cloudlets are located close to end users, they offer low-latency and high-bandwidth connections, which significantly improve performance and user experience compared to relying solely on distant cloud servers.