Host-Aware/Drive-Managed Shingled Magnetic Recording (SMR) drives can accept non-sequential writes using a buffer called media cache. Data in the media cache will be migrated to its designated location by a cleaning process if the buffer is full (blocking cleaning) or the drive is idle (idle cleaning). However, blocking cleanings can severely extend the I/O response time. Therefore, it is crucial to fully understand the cleaning process and find ways of mitigating the caused performance degradation. In this article we further evaluate the cleaning process and propose a potential remedy scheme called Idler on Host-Aware SMR drives. Idler adaptively induces idle cleanings based on dynamic workload characteristics and media cache usages to reduce the severity of blocking cleanings. Our evaluations show that in the workloads with a small non-sequential write ratio (about 10 percent), Idler can reduce the tail response time and the workload finish time by 56–88 and 10–23 percent, respectively, compared with those without such control. With the help of an external write buffer on an SSD, the tail response time of SMR drives with Idler can be closer to that of conventional disk drives.

Host-aware shingled magnetic recording (HA-SMR) drives deliver higher disk capacity and expose internal zones to host, but their performance can be severely degraded when subjected to nonsequential writes (NSWs). Using solid state drives (SSDs) as a cache layer to proactively cache NSWs can alleviate this performance degradation. However, existing SSD caching designs are not capable of identifying and selectively caching NSWs, which incurs ineffectual SSD write traffic and excessively wears the limited write endurance of SSD. In this paper, we propose an endurable SMR-oriented SSD Caching (SMRC) framework. SMRC leverages the intrinsic host-aware property of HA-SMR drives to filter both sequential writes and innocuous NSWs out of SSD. Besides, SMRC reorganizes the cached NSWs to evict them with cleaning-friendly write traffic to HA-SMR drives. Our experimental results show that SMRC can effectively reduce SSD write traffic and improve system performance compared with existing SSD caching designs.

In HPC systems and hyper-scale data centers, the adoption of high-performance NVMe SSDs and high-speed networks has shifted storage bottlenecks to the network stack. Under high-concurrency workloads, frequent interrupt processing exhausts CPU resources while protocol-level control–data dependencies in the NVMe over TCP write path introduce additional serialization penalties. Existing optimizations either require specialized hardware, dedicate CPU cores to user-space polling, or apply semantically blind batching that delays time-sensitive control messages. We present SENS, a Semantic-aware NVMe over TCP Scheduler embedded within the NVMe over TCP driver of the Linux kernel. SENS combines two mechanisms: (1) PDU vectorization, which aggregates discrete Protocol Data Units into memory vectors before network transmission, amortizing per-I/O system call overhead and reducing soft-interrupt frequency; and (2) instruction-aware dispatch, which detects control PDUs such as R2T and triggers an early flush of the aggregation window, mitigating the serialization penalty on the write path. A prototype evaluation with physical NVMe SSDs and 100 GbE networks shows that SENS saturates the SSD throughput ceiling using 4–5 CPU cores, halving the host-side core budget compared to the native TCP driver. With a RAMDisk backend that removes storage-media constraints, SENS sustains up to 2.5× higher concurrent IOPS. These results show that exposing storage-protocol semantics to the batching layer improves the scalability of NVMe over TCP without additional hardware.