Data deduplication is an effective technique for reducing redundant data storage space in various storage systems. Generally, deduplication consists of four steps: chunking, fingerprinting, fingerprint lookup, and data management. Recently, Non-volatile Memory (NVM) as an emerging storage device has received widespread attention. Directly applying the deduplication technique on NVM for storage cost savings faces many challenges: (a) deduplication on NVM devices suffers from computation bottleneck instead of the I/O bottleneck faced by deduplication on traditional storage devices (such as HDD and SSD); (b) new fingerprint indexes and metadata are required to be re-designed to adapt to NVM characteristics; (c) inline deduplication on NVM is more sensitive to the latency. To solve these challenges, we propose a novel Samp ling prediction-based inline data Dedup lication method ( SampDedup ) on NVM devices. It aims to ensure high deduplication ratios while reducing computation costs and latency by optimizing data chunking , fingerprinting , and fingerprint lookup . (a) For data chunking , a sampling prediction-based chunking method ( SampChunk ) is proposed to leverage chunk similarity to distinguish duplicate chunks and skip them for chunking. This method can be easily integrated into most sliding-window based and non-window based CDC chunking algorithms. (b) For fingerprinting , the commonly used SHA-1 algorithm is further optimized to reduce the extra computational overhead introduced by SampChunk, and an asynchronous fingerprinting method is proposed to reduce the fingerprinting latency of unique chunks. (c) For fingerprint lookup , we design a header fingerprint index and metadata table for each data chunk constructed by SampChunk on NVM, and we use a fast-read buffer to replace the traditional slow LRU cache to improve search efficiency. Experiments on four real-world datasets demonstrate that SampDedup consistently presents high inline data deduplication ratios on NVM with different workloads and data partitioning algorithms while saving more than 90% chunking time compared with state-of-the-art deduplication baselines.

Recent online knowledge distillation (OKD) methods focus on capturing rich and useful intermediate information by performing multi-layer feature learning. Existing works only consider intermediate layer feature maps between the same layers and ignore valuable information across layers, which results in the lack of appropriate cross-layer supervision in detail and the process of learning. Besides, this manner provides insufficient supervision information to supervise the learning of student, since it fails to construct a qualified teacher. In this work, we propose a Deep Cross-layer Collaborative Learning network (DCCL) for OKD, which efficiently exploits fruitful knowledge of peer student models by keeping appropriate intermediate cross-layer supervision. Specifically, each student gradually integrates its own features at different layers for feature matching, so as to effectively utilize features in low and high levels for learning more composite knowledge. Moreover, we assign a collaborative knowledge learning strategy, in which a qualified teacher is established via fusing the features of last convolution layers for enhancing high-level representation. In this way, all student models continuously transfer the rich teacher’s internal representation as well as capture its dynamic growth process, and in turn assist the learning of the fusion teacher to further supervise students. In the experiments, our proposed DCCL has shown great generalization ability with various backbone models on CIFAR-100, Tiny ImageNet and ImageNet, and also demonstrated superior performance against mainstream OKD works. Our code is available here:https://github.com/nanxiaotong/DCCL.