AdaptMD: Balancing Space and Performance in NUMA Architectures With Adaptive Memory Deduplication

Abstract

Memory deduplication effectively relieves the memory space bottleneck by removing duplicate pages, especially in virtualized systems in which virtual machines run the same OS and similar applications. However, due to the non-uniform access latencies in NUMA architectures, memory deduplication poses a trade-off between memory savings and access performance: global deduplication across NUMA nodes realizes high memory savings, but leads to frequent cross-node remote access after deduplication and results in performance degradations. In contrast, local deduplication avoids remote access, but limits deduplication effectiveness. We design AdaptMD, an adaptive memory deduplication system that addresses the space-performance trade-off in NUMA architectures. AdaptMD leverages hotness awareness to globally deduplicate only cold pages to reduce remote access. It also migrates similar applications to the same NUMA node to allow local deduplication without remote access. We further make AdaptMD readily configurable to address various deployment scenarios. Experiments show that AdaptMD achieves high memory savings as in global deduplication, while achieving similar access performance as in local deduplication.