In artificial intelligence and computer science, a queue is a fundamental data structure that organizes elements in a specific, ordered sequence. The defining characteristic of a queue is its “first-in, first-out” (FIFO) principle. This means that the first element added to the queue will be the first one to be removed. Queues are used in countless AI systems and applications to manage tasks, data, or computational resources in a fair and efficient way.
A typical example of a queue is the line of print jobs sent to a printer. The first document submitted is printed before the others. In AI and machine learning workflows, queues appear in many places, from scheduling model training jobs in the cloud to managing incoming user requests for a chatbot. When a deep learning model is trained using mini-batch gradient descent, training data may be loaded into a queue to ensure the GPU or TPU always has data to process without idle time.
Queues are also crucial in reinforcement learning, where experience replay buffers are often implemented as queues. Here, experiences (state, action, reward, next state) are stored in a queue so that the learning agent can sample and learn from past experiences, not just the latest ones. This improves the stability and efficiency of learning.
Under the hood, queues can be simple linear structures or more advanced concurrent queues designed to handle multiple processes or threads safely. Many programming libraries and deep learning frameworks like TensorFlow and PyTorch provide built-in queue implementations. These can be used to manage asynchronous data loading, coordinate computation steps, or distribute work across multiple devices.
Understanding queues helps when building scalable AI systems. For example, in a machine learning serving pipeline, a queue might buffer incoming prediction requests so they can be processed smoothly by a pool of model servers. This prevents system overload and ensures requests are handled in the order received, leading to fair and predictable behavior.
Queues differ from stacks, which use a “last-in, first-out” (LIFO) approach. While stacks are suited for problems like function call management, queues fit scenarios where tasks must be handled in arrival order. Queues can be implemented as arrays, linked lists, or even as distributed queues in cloud-based architectures.
Some advanced queue types include priority queues, where items are processed by importance rather than arrival time. However, the basic queue remains one of the simplest and most vital structures in AI workflows, supporting everything from data preprocessing pipelines to distributed machine learning training.
