Latency Critical

Latency-critical systems prioritize minimizing delays in processing and communication, a crucial aspect for applications like augmented reality and autonomous vehicles. Current research focuses on developing adaptive resource allocation strategies, often employing deep reinforcement learning and graph neural networks, to optimize performance under uncertain conditions and diverse workloads, including federated learning scenarios. These advancements aim to improve the reliability and efficiency of latency-sensitive services across various domains, from edge computing to 6G networks, by dynamically managing computational resources and network traffic to meet stringent timing requirements.