In today's data-driven era, the explosive growth of global data volume has led to an increasing consumption of computing and storage resources. Effective management of virtual machines (VMs) memory usage is critical for cloud vendors to optimize system performance and resource utilization. Existing memory prefetching methods often slow down system performance, creating a difficult balance between maintaining service quality and optimizing resource use. For instance, Leap, which primarily utilizes address information, performs poorly in VM environments. The main issue is the performance drop caused by the reuse of memory resources in virtualized environments, a common situation in public clouds.

Scale-out applications, such as various big data systems and memory computing programs comprise an important software stack in clouds.Such applications usually have large memory data footprint as well as code sizes, thus stressing the CPU's TLB efficiency.In this paper, we experimentally evaluate how various TLB design choices in modern off-the-shelf x86 CPUs impact the performance of scale-out applications.The findings aim to guide the partitioning schemes and capacity planning of TLBs, and software-hardware co-design for emerging applications.