Thermostat

Abstract

The advent of new memory technologies that are denser and cheaper than commodity DRAM has renewed interest in two-tiered main memory schemes. Infrequently accessed application data can be stored in such memories to achieve significant memory cost savings. Past research on two-tiered main memory has assumed a 4KB page size. However, 2MB huge pages are performance critical in cloud applications with large memory footprints, especially in virtualized cloud environments, where nested paging drastically increases the cost of 4KB page management. We present Thermostat, an application-transparent huge-page-aware mechanism to place pages in a dual-technology hybrid memory system while achieving both the cost advantages of two-tiered memory and performance advantages of transparent huge pages. We present an online page classification mechanism that accurately classifies both 4KB and 2MB pages as hot or cold while incurring no observable performance overhead across several representative cloud applications. We implement Thermostat in Linux kernel version 4.5 and evaluate its effectiveness on representative cloud computing workloads running under KVM virtualization. We emulate slow memory with performance characteristics approximating near-future high-density memory technology and show that Thermostat migrates up to 50% of application footprint to slow memory while limiting performance degradation to 3%, thereby reducing memory cost up to 30%.