Workload Aware

Workload-aware systems aim to optimize resource allocation and performance by dynamically adapting to the characteristics of incoming tasks. Current research focuses on applying machine learning, particularly reinforcement learning, to develop adaptive scheduling policies and resource allocation strategies across diverse applications, from cloud computing and large language model inference to key-value stores and edge computing. This approach promises significant improvements in efficiency, latency, and energy consumption in various computing environments, impacting both the design of future systems and the performance of existing ones.