Atharv Gyan

Edge computing is a decentralized computing model that processes data closer to the source where it is generated (the "edge" of the network), rather than sending it to centralized data centers or cloud servers. This model is particularly beneficial in scenarios involving the Internet of Things (IoT) , where real-time processing, low latency, and bandwidth efficiency are critical. Key Features of Edge Computing Proximity to Data Source : Edge devices, such as IoT sensors or gateways, process and analyze data locally rather than sending it to the cloud.  Reduced Latency : By processing data near its source, edge computing minimizes delays, which is essential for time-sensitive applications like autonomous vehicles or industrial automation. Bandwidth Optimization : Only critical data is sent to the cloud for further analysis or storage, reducing the load on networks. Enhanced Security and Privacy : Since data is processed locally, it reduces the risk of interception during trans...

Edge computing represents a paradigm shift in the way computing resources are deployed and managed, particularly in relation to cloud architecture. Traditionally, cloud computing involves centralized data centers where processing, storage, and networking functions are concentrated. However, with the rise of edge computing, computing tasks are distributed closer to the data source or end-user devices, resulting in a more decentralized architecture. This shift has significant implications for cloud architecture, impacting aspects such as latency, scalability, security, and service delivery. One of the key impacts of edge computing on cloud architecture is the reduction of latency. By moving computing resources closer to the point of data generation or consumption, edge computing minimizes the distance that data must travel, resulting in lower latency and improved response times. This is particularly important for applications that require real-time or near-real-time processing, such as I...