In AI systems, queues are fundamental structures used to handle tasks, processes, or data streams efficiently, ensuring orderly and sequential processing. They play a critical role in managing execution workflows, especially in environments involving concurrent processing, such as neural network training, message passing, and data preprocessing pipelines. Queues can also be implemented in priority formats, where tasks are processed based on predefined criteria rather than order of arrival, enhancing flexibility and adaptability in AI applications. In distributed AI frameworks, such as those used for large-scale simulations or model training, queues help in balancing load, optimizing resource utilization, and minimizing idle time by distributing tasks across available resources systematically.
The concept of queues dates back to operations research and computer science foundations in the late 1950s and early 1960s, gaining prominence with the advent of computer operating systems that required efficient scheduling algorithms. Their application in AI became especially relevant in the 1980s as the complexities of AI tasks necessitated more organized management methods.
Prominent contributions to the development of queue theory come from Agner Krarup Erlang in the early 20th century who laid the foundational concepts of queuing systems. His work in telecommunications directly influenced subsequent developments in computer systems and by extension, AI applications.