High service availability is crucial for cloud systems. A typical cloud system uses a large number of physical hard disk drives and solid state drives. Disk errors are one of the most important causes that lead to service unavailability. Disk error (such as reallocate sector error and long access latency) can be seen as a form of gray failure, which are fairly subtle failures that are hard to be detected, even when applications are afflicted by them. In this talk, we will introduce an approach to predict disk errors proactively to avoid severe damage to the cloud system. The ability to predict faulty disks enables live migration of existing virtual machines and allocation of new virtual machines to the healthy disks, therefore improving service availability. To build an accurate online prediction model, we utilize both disk-level sensor (SMART) data as well as system level signals. We develop a cost-sensitive ranking-based machine learning model that can learn the characteristics of faulty disks in the past and rank the disks based on their error-proneness in the near future. We evaluate our approach using real-world data collected from a production cloud system.