DRLPart: A Deep Reinforcement Learning Framework for Optimally Efficient and Robust Resource Partitioning on Commodity Servers

Workload consolidation is a commonly used approach for improving resource utilization of commodity servers. However, colocated workloads often suffer from significant performance degradations due to resource contention, which makes resource partitioning an important research problem. Partitioning multiple resources coordinately is particularly challenging due to the complex contention behaviors and huge solution space, which is not well-addressed in the literature.