👉 Size computing, also known as data-centric computing, is a paradigm that shifts the focus from processing power to the amount of data being handled and processed. In traditional computing, performance is often measured by the speed of the processor or the amount of memory it can handle. However, size computing emphasizes the efficiency and effectiveness of data storage, transfer, and analysis. It involves designing systems that can manage large volumes of data with minimal computational overhead, often leveraging distributed storage and parallel processing techniques. By prioritizing data management, size computing aims to reduce latency, increase throughput, and optimize resource utilization, making it particularly relevant for applications like big data analytics, machine learning, and real-time data processing.
