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Public cloud providers seek to meet stringent performance requirements andlow hardware cost. A key driver of performance and cost is main memory. Memorypooling promises to improve DRAM utilization and thereby reduce costs. However,pooling is challenging under cloud performance requirements. This paperproposes Pond, the first memory pooling system that both meets cloudperformance goals and significantly reduces DRAM cost. Pond builds on theCompute Express Link (CXL) standard for load/store access to pool memory andtwo key insights. First, our analysis of cloud production traces shows thatpooling across 8-16 sockets is enough to achieve most of the benefits. Thisenables a small-pool design with low access latency. Second, it is possible tocreate machine learning models that can accurately predict how much local andpool memory to allocate to a virtual machine (VM) to resemble same-NUMA-nodememory performance. Our evaluation with 158 workloads shows that Pond reducesDRAM costs by 7% with performance within 1-5% of same-NUMA-node VM allocations.

Pond: CXL-Based Memory Pooling Systems for Cloud Platforms