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Real-time computing

Real-time computing

(also real-time processing, RTC)

Real-time computing definition

Real-time computing refers to a computing system category that processes information and reacts to input within a particular time constraint, frequently measured in milliseconds. These systems are engineered to manage tasks with time sensitivity and play a crucial role in applications that demand prompt responses. Real-time computing is categorized into two types: hard real-time computing, where missing deadlines may lead to disastrous outcomes, and soft real-time computing, in which infrequent deadline lapses are acceptable.

Real-time computing examples

  • Air traffic control systems: They rely on real-time computing to manage aircraft movements and ensure safe travel by providing timely updates on flight data and weather conditions.
  • Industrial automation: Real-time computing is used in controlling and monitoring industrial processes, such as assembly lines or chemical plants, to maintain efficiency and safety.
  • Online gaming: Multiplayer online games rely on real-time computing to deliver smooth gameplay and synchronize actions between players.

Real-time computing vs. non-real-time computing

Real-time computing systems prioritize time-sensitive tasks and aim to complete them within a predetermined time frame, while non-real-time systems do not have such constraints. In non-real-time systems, tasks may be executed in any order, and response times may vary, making them unsuitable for time-critical applications.

Advantages of real-time computing

  • Improved efficiency: Real-time systems can handle high data throughput and provide timely responses, increasing the efficiency of time-sensitive applications.
  • Enhanced safety: In safety-critical applications like air traffic control, real-time computing can prevent accidents by ensuring timely responses to changing conditions.

Tips for achieving real-time computing

  • Use a real-time operating system (RTOS) designed to handle real-time tasks and manage system resources efficiently.
  • Optimize hardware and software components to reduce latency and improve response times.

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