Efficient Disaggregated Memory Eviction with Glitter
Linxuan Zhong, Wenxin Li, Yulong Li, Jiawen Shen, Song Zhang, Wenyu Qu, Yitao Hu · 2024
Memory disaggregation, a promising technique allowing applications to use remote memory, is increasingly appealing in datacenters due to its high resource utilization. Operationally, the application’s host server constantly evicts unused data to remote to make room for memory allocation of new pages. Inefficient evictions allow memory usage to hit its limit, resulting in application blocking, which brings severe throughput degradation. However, most existing works neglect the importance of eviction. They offload the eviction to a background thread and set a fixed trigger timing, rendering a belated eviction. Worse still, they overlook the impact of network congestion on eviction efficiency, making their strategy flawed in large-scale scenarios. In this paper, we present Glitter, an adaptive, multi-level awareness eviction solution that accelerates applications by minimizing the overhead of application blocking from host and network aspects. For host, Glitter presents an adaptive eviction threshold adjustment to optimize the eviction timing, reducing the occurrence of application blocking. For network, Glitter adopts an eviction flow scheduling to address the hazards posed by flow contention at switches, decreasing the duration of each application blocking. Through comprehensive experiments, Glitter gives an average 1.4 throughput boost to Fastswap, a state-of-the-art disaggregated×memory system.