Autonomous computing and autonomic computing are related concepts in the field of computer science and share some similarities. However, they have distinct meanings and applications and thus, are not interchangeable. Autonomous computing means that a machine, a device, or software can operate with little or no human control — in short, it can independently. For example, a self-driving car operates autonomously thanks to equipment like advanced sensors, cameras, radar, and other software systems, which enable it to make decisions based on real-time data and navigate complex and dynamic environments without constant human oversight or intervention. Another example of an autonomous computing system is a drone. Drones are equipped with sensors, GPS, cameras, and often artificial intelligence (AI) capabilities, which allow them to perform a variety of tasks independently. Autonomous systems may or may not exhibit autonomic computing features, but the emphasis is on independence and self-governance. Autonomic computing, by contrast, is not only about operating independently, but also having the awareness and adaptability to respond to its environment. Defined by IBM in a 2001 manifesto, “The Vision of Autonomic Computing,” there are four areas of autonomic computing: self-configuration, self-healing, self-optimization, and self-protection. Key characteristics of every autonomic computing system are automation, adaptivity, and awareness. A car, for example, that self-tunes and refuels without intervention is operating autonomically. Same goes for a server system that can dynamically allocate resources, detect and recover from faults, and optimize its performance without requiring constant manual adjustments.