Disk failures, the most common and major failures in storage systems, increase the risk of service interruption and data loss, and bring additional maintenance costs, which reduces system reliability. Disk failure prediction methods aim to forecast failures, initiating prompt data migration and disk replacement. Existing methods continuously optimize the models with different sampling methods and modeling algorithms. However, due to issues such as inaccurate sample labeling, insufficient data sampling, and improper sample segmentation, the predictive capabilities of existing models within the lookahead-window time are unstable and decline as the lookahead-window time increases. To address this, we propose LWCM (Lookahead-Window Constrained Model) to improve the predictability and stability of failure prediction models within the lookahead-window time. LWCM leverages dynamic sample relabeling methods based on lookahead-window time constraints and failure symptom durations to modify inaccurate sample labels. LWCM utilizes effective sample data by using the two-phase data sampling method including initial expectation sampling and subsequent segmented resampling. LWCM employs dynamic weighted optimization in backpropagation to enhance the predictability and stability of the disk failure prediction model. Experimental results show that LWCM has better failure prediction performance. The true positive and false positive rates surpass those of the offline-RF model by 38.7% and 92.4%, respectively. Furthermore, LWCM demonstrates its applicability across disk models while maintaining stability within the lookahead constraint window.

Shingled magnetic recording (SMR) can effectively increase the capacity of hard disk drives (HDDs). Hostaware SMR (HA-SMR) is expected to be more popular than other SMR models because of its backward compatibility and new SMR-specific APIs. However, an HA-SMR drive often suffers performance degradation under write-intensive workloads because of frequent non-sequential writes buffered in the disk cache. The non-sequential writes mainly come from update writes, small random writes and out-of-order writes. In this paper, we propose a hybrid storage system called ROCO which aims to use a solid state drive (SSD) cache to improve the performance of an HA-SMR drive. ROCO reorders out-of-order writes belonging to the same zone and uses the SSD cache to absorb update writes and small random writes. We also design a data replacement algorithm called CREA for the SSD cache. CREA first conducts zone-oriented hot/cold data identification to identify cold-cached zones and hot-cached zones, and then evicts data blocks belonging to colder zones with higher priorities that can be sequentially written or written through host-side read-modify-write operations. It gives the lowest priority to data blocks belonging to the hottest-cached zone that have to be non-sequentially written. Experimental results show that ROCO can effectively reduce non-sequential writes to the HA-SMR drive and improve the performance of the HA-SMR drive.