Large pages and lightweight memory management in virtualized environments

Abstract

Large pages have long been used to mitigate address translation overheads on big-memory systems, particularly in virtualized environments where TLB miss overheads are severe. We show, however, that far from being a panacea, large pages are used sparingly by modern virtualization software. This is because large pages often preclude lightweight memory management, which can outweigh their Translation Look aside Buffer (TLB) benefits. For example, they reduce opportunities to de duplicate memory among virtual machines in overcommitted systems, interfere with lightweight memory monitoring, and hamper the agility of virtual machine (VM) migrations. While many of these problems are particularly severe in overcommitted systems with scarce memory resources, they can (and often do) exist generally in cloud deployments. In response, virtualization software often (though it doesn't have to) splinters guest operating system (OS) large pages into small system physical pages, sacrificing address translation performance for overall system-level benefits. We introduce simple hardware that bridges this fundamental conflict, using speculative techniques to group contiguous, aligned small page translations such that they approach the address translation performance of large pages. Our Generalized Large-page Utilization Enhancements (GLUE) allow system hypervisors to splinter large pages for agile memory management, while retaining almost all of the TLB performance of unsplintered large pages.