For consistent sub-millisecond latency and maximum IOPS for random I/O, direct-attached NVMe drives provide the lowest latency and highest performance. A software-defined storage pool using commodity servers with NVMe drives offers a cost-effective, scalable, and high-performance foundation compared to proprietary all-flash arrays (which are also performant but often at a higher cost) or solutions involving caching or spinning disks.

Log-structured systems write data sequentially to a ‘log,’ which is excellent for write performance, especially for sequential and small random writes. However, to reclaim space from deleted or outdated data, a background process called garbage collection is required. This process involves reading valid data from old segments and rewriting it, leading to write amplification—a key consideration for drive endurance and sustained performance.

A centralized metadata database can become a scalability bottleneck and a single point of failure for the entire storage system. A distributed metadata cluster spreads the metadata across multiple nodes, enhancing both scalability (as capacity grows, metadata handling capacity can grow with it) and availability (the failure of one node does not cripple metadata services).

A 15-minute RPO allows for a small amount of data loss. Synchronous replication (option 0) would guarantee zero data loss but is impractical over 500 miles due to latency impacts on the primary application. Asynchronous replication sends data to the remote site after it’s written locally, meeting the 15-minute RPO without imposing the distance-related latency penalty of synchronous replication. Options 2 and 3 have RPOs measured in hours or days.

In a shared-nothing architecture, each node in a cluster is self-sufficient, with its own dedicated compute, memory, and storage resources. Nodes coordinate over a network but do not compete for access to shared hardware (like a common storage bus or memory). This design simplifies scalability (just add more nodes) and improves resilience, as the failure of one node does not directly compromise the resources of another.

Deduplication excels at finding redundancy in large, similar datasets like full VM backups. However, once the common base images are stored, subsequent backups (especially incremental or incremental-forever backups) contain mostly new or changed data unique to that point in time. This new data has less redundancy to deduplicate against the existing pool, leading to a lower deduplication ratio for subsequent jobs.

The symptoms point directly to a back-end bottleneck. High queue depth and high drive utilization for a specific LUN indicate that the I/O requests are piling up waiting for the physical drives that compose that LUN to service them. The problem is isolated to one LUN, ruling out array-wide issues like front-end network, front-end ports, or controller CPU (which would typically affect multiple LUNs). The workload is likely exceeding the performance capabilities (IOPS/throughput) of that particular drive group.

Storage technologies are optimized for different patterns. For example, large sequential I/O benefits from high-throughput architectures and can perform well on HDDs. Small, random I/O requires low latency and high IOPS, favoring SSDs. A write-heavy workload demands different endurance and caching considerations than a read-heavy one. Selecting storage without analyzing the workload is like choosing a vehicle without knowing if you need a sports car, a truck, or a minivan.

In scale-out file systems, data is striped across multiple storage nodes for performance and capacity. The metadata server’s critical job is to manage the file system hierarchy (directories, files) and maintain the map that tracks which pieces of each file are located on which specific data nodes. When a client requests a file, it queries the metadata server first to get this map, then fetches the data directly from the appropriate nodes.

For consistent sub-millisecond latency and maximum IOPS for random I/O, direct-attached NVMe drives provide the lowest latency and highest performance. A software-defined storage pool using commodity servers with NVMe drives offers a cost-effective, scalable, and high-performance foundation compared to proprietary all-flash arrays (which are also performant but often at a higher cost) or solutions involving caching or spinning disks.0