Browsing by Author "Qin, Yaobin"

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    HyperProtect: A Large-scale Intelligent Backup Storage System
    (2022-01) Qin, Yaobin
    In the current big data era, huge financial losses are caused when data becomes unavailable at the original storage side. Protecting data from loss plays a significantly important role in ensuring business continuity. Businesses generally employ third-party backup services to save their data in remote storage and hopefully retrieve the data in a tolerable time range when the original data cannot be accessed. To utilize these backup services, backup users have to handle many kinds configurations to ensure the effective backup of the data. As the scale of backup systems and the volume of backup data continue to grow significantly, the traditional backup systems are having difficulties satisfying the increasing demand requirement of backup users. The fast improvement of machine or deep learning techniques has made them successful in many areas, such as image recognition, object detection, and natural language processing. Compared with other system environments, the backup system environment is more consistent due to the backup nature; the backup data contents are not changed considerably, at least in the short run. Hence, we collected data from real backup systems and analyzed the backup behavior of backup users. By using machine learning techniques, we discovered that some patterns and features can be generalized from the backup environment. We used them to as a guid in the design of an intelligent agent called HyperProtect, which aims to improve the service level provided by the backup systems. To apply machine or deep learning techniques to enhance the service level of the backup systems, we first improved the stability and predictability of the backup environment by proposing a novel dynamic backup scheduling and high-efficiency deduplication. Backup scheduling and deduplication are important backup techniques in backup systems. Backup scheduling determines which backup starts first and which storage is assigned to that backup for improving the backup efficiency. Deduplication is used to remove the redundancy of the backup data to save the storage space. Besides the backup efficiency and storage overhead, we considered maintaining the stability and predictability of the backup environment when processing the backup scheduling and deduplication. When the backup environment became more stable, we applied machine learning to improve the reliability and efficiency of the large-scale backup system. We analyzed data protection system reports written over two years and collected from 3,500 backup systems. We found that inadequate capacity is among the most frequent causes of backup failure. We highlighted the characteristics of backup data and used the examined information to design a backup storage capacity forecasting structure for better reliability of backup systems. According to our observation of an enterprise backup system, for a newly created client, there are no historical backups, so the prefetching algorithm has no reference basis to perform effective fingerprint prefetching. We discovered a backup content correlation between clients from a study of the backup data. We propose a fingerprint prefetching algorithm to improve the deduplication rate and efficiency. Here machine learning and statistical techniques are applied to discover backup patterns and generalize their features. The above efforts introduced machine learning for backup systems. We also considered the other direction, namely, backup systems for machine learning. The advent of the Artificial Intelligence (AI) era has made it increasingly important to have an efficient backup system to protect training data from loss. Furthermore, maintaining a backup of the training data makes it possible to update or retrain the learned model as more data are collected. However, a huge backup overhead will result from always making a complete copy of all collected daily training data for backup storage, especially because data typically contains highly redundant information that does not contribute to model learning. Deduplication is a common technique of reducing data redundancy in modern backup systems. However, existing deduplication methods are invalid for training data. Hence, we propose a novel deduplication strategy for the training data used for learning in a deep neural network classifier.
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    Radial Basis Function (RBF) Neural Networks Based on the Triple Modular Redundancy Technology (TMR)
    (2015) Qin, Yaobin
    Neural networks are family statistical learning algorithms and structures and are used to estimate or approximate functions and pattern classification. The Neural network system is constructed through interconnected neurons and training weights. The paper will present the improvement of recognition rate, recognition time and hardware overhead through introducing TMR technology into the conventional RBF neural network which is a simple neural network only consisting of three layers.