This article presents a Differentiated Storage Services architecture for file and storage systems. By classifying data at the block-level, a filesystem can request that different classes of data (e.g., file, directory, executable, text) be handled with different policies (e.g., low-latency versus highbandwidth), and it is left to the storage system to enforce these policies. Our approach assumes that an I/O classifier can be included in-band with each I/O request (e.g., using a field in the SCSI block command set) and that the policy for each class can be specified out-of-band through the management interface of the storage system. We describe our prototypes based on Linux Ext3, Windows NTFS, and hybrid storage systems composed of rotating and solid-state disks. With very little modification, filesystems can identify latency sensitive I/O classes (e.g., small files, directories, metadata, and the journal) and request that the storage system provision the solid-state storage for just these classes; and this is simply one of many possibilities. As part of our ongoing work, across a variety of file and storage systems, we are exploring other policies and mechanisms that can be used to improve application performance, reliability, and security.

Cysts located within the lesser tuberosity are rare and are indicative of subscapularis and supraspinatus tendon abnormalities.

No abstract

High-performance flash-based key-value stores in data-centers utilize large amounts of DRAM to cache hot data. However, motivated by the high cost and power consumption of DRAM, server designs with lower DRAM-per-compute ratio are becoming popular. These low-cost servers enable scale-out services by reducing server workload densities. This results in improvements to overall service reliability, leading to a decrease in the total cost of ownership (TCO) for scalable workloads. Nevertheless, for key-value stores with large memory footprints, these reduced DRAM servers degrade performance due to an increase in both IO utilization and data access latency. In this scenario, a standard practice to improve performance for sharded databases is to reduce the number of shards per machine, which degrades the TCO benefits of reduced DRAM low-cost servers. In this work, we explore a practical solution to improve performance and reduce the costs and power consumption of key-value stores running on DRAM-constrained servers by using Storage Class Memories (SCM). SCMs in a DIMM form factor, although slower than DRAM, are sufficiently faster than flash when serving as a large extension to DRAM. With new technologies like Compute Express Link, we can expand the memory capacity of servers with high bandwidth and low latency connectivity with SCM. In this article, we use Intel Optane PMem 100 Series SCMs (DCPMM) in AppDirect mode to extend the available memory of our existing single-socket platform deployment of RocksDB (one of the largest key-value stores at Meta). We first designed a hybrid cache in RocksDB to harness both DRAM and SCM hierarchically. We then characterized the performance of the hybrid cache for three of the largest RocksDB use cases at Meta (ChatApp, BLOB Metadata, and Hive Cache). Our results demonstrate that we can achieve up to 80% improvement in throughput and 20% improvement in P95 latency over the existing small DRAM single-socket platform, while maintaining a 43–48% cost improvement over our large DRAM dual-socket platform. To the best of our knowledge, this is the first study of the DCPMM platform in a commercial data center.

No abstract