Yong Xu and Kaixin Sui, Microsoft Research, China; Randolph Yao, Microsoft Azure, USA; Hongyu Zhang, The University of Newcastle, Australia; Qingwei Lin, Microsoft Research, China; Yingnong Dang, Microsoft Azure, USA; Peng Li, Nankai University, China; Keceng Jiang, Wenchi Zhang, and Jian-Guang Lou, Microsoft Research, China; Murali Chintalapati, Microsoft Azure, USA; Dongmei Zhang, Microsoft Research, China
High service availability is crucial for cloud systems. A typical cloud system uses a large number of physical hard disk drives. Disk errors are one of the most important reasons that lead to service unavailability. Disk error (such as sector error and latency error) 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 paper, we propose to predict disk errors proactively before they cause more severe damage to the cloud system. The ability to predict faulty disks enables the 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 systemlevel 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. The results confirm that the proposed approach is effective and outperforms related methods. Furthermore, we have successfully applied the proposed approach to improve service availability of Microsoft Azure.
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