It is important to utilize the idle time of a workload to improve the system performance. In the article, we will explore multiple idle time detection methods to predict the idle time of the real I/O workloads. The objective is to build a stable idle time detection platform by investigating the impact of multiple representative methods to pursue a more stable prediction accuracy. The experimental results show that the prediction accuracy of the proposed platform can be stable between 60% and 80%.

DM-SMR (device-managed shingled magnetic recording) disks allocate a portion of disk space as the persistent cache (PC) to address the issue of overlapping tracks during data updates. When the PC space becomes insufficient, a space cleaning is triggered to reclaim its invalid space. However, the space cleaning is time-consuming and contributes to the long-tail latency of DM-SMR disks. In the article, we will propose a space-grained cleaning method that leverages various idle periods to effectively reduce the long-tail latency of DM-SMR disks. The objective is to perform a proper space-grained cleaning for a suitable space region at an appropriate time period, thereby preventing delays in subsequent I/O requests and reducing the long-tail latency associated with DM-SMR disks. The experimental results demonstrate a substantial reduction in the long-tail latency of DM-SMR disks through the proposed method.