The unrelenting growth of the memory needs of emerging datacenter applications, along with ever increasing cost and volatility of DRAM prices, has led to DRAM being a major infrastructure expense. Alternative technologies, such as NVMe SSDs and upcoming NVM devices, offer higher capacity than DRAM at a fraction of the cost and power. One promising approach is to transparently offload colder memory to cheaper memory technologies via kernel or hypervisor techniques. The key challenge, however, is to develop a datacenter-scale solution that is robust in dealing with diverse workloads and large performance variance of different offload devices such as compressed memory, SSD, and NVM. This paper presents TMO, Meta's transparent memory offloading solution for heterogeneous datacenter environments. TMO introduces a new Linux kernel mechanism that directly measures in realtime the lost work due to resource shortage across CPU, memory, and I/O. Guided by this information and without any prior application knowledge, TMO automatically adjusts how much memory to offload to heterogeneous devices (e.g., compressed memory or SSD) according to the device's performance characteristics and the application's sensitivity to memory-access slowdown. TMO holistically identifies offloading opportunities from not only the application containers but also the sidecar containers that provide infrastructure-level functions. To maximize memory savings, TMO targets both anonymous memory and file cache, and balances the Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Resource isolation is a fundamental requirement in datacenter environments. However, our production experience in Meta's largescale datacenters shows that existing IO control mechanisms for block storage are inadequate in containerized environments. IO control needs to provide proportional resources to containers while taking into account the hardware heterogeneity of storage devices and the idiosyncrasies of the workloads deployed in datacenters. The speed of modern SSDs requires IO control to execute with low-overheads. Furthermore, IO control should strive for work conservation, take into account the interactions with the memory management subsystem, and avoid priority inversions that lead to isolation failures.To address these challenges, this paper presents IOCost, an IO control solution that is designed for containerized environments and provides scalable, work-conserving, and low-overhead IO control for heterogeneous storage devices and diverse workloads in datacenters. IOCost performs offline profiling to build a device model and uses it to estimate device occupancy of each IO request. To minimize runtime overhead, it separates IO control into a fast per-IO issue path and a slower periodic planning path. A novel work-conserving budget donation algorithm enables containers to dynamically share unused budget. We have deployed IOCost across the entirety of Meta's datacenters comprised of millions of machines, upstreamed IOCost to the Linux kernel, and open-sourced our device-profiling tools. IOCost has been running in production Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

No abstract

No abstract