Resource Contention

Resource contention, the competition for limited resources among multiple users or processes, is a critical challenge across diverse computing domains, from training large-scale machine learning models to managing wireless network spectrum. Current research focuses on developing algorithms and system architectures that mitigate contention, including reinforcement learning for dynamic resource allocation, novel scheduling heuristics for federated learning, and memory-centric approaches for optimizing deep neural network execution. Addressing resource contention is crucial for improving efficiency, performance, and fairness in various applications, ranging from medical AI systems to large-scale data centers and wireless communication networks.