Inline deduplication removes redundant data in real-time as data is being sent to the storage system. However, it causes data fragmentation: logically consecutive chunks are physically scattered across various containers after data deduplication. Many rewrite algorithms aim to alleviate the performance degradation due to fragmentation by rewriting fragmented duplicate chunks as unique chunks into new containers. Unfortunately, these algorithms determine whether a chunk is fragmented based on a simple pre-set fixed value, ignoring the variance of data characteristics between data segments. Accordingly, when backups are restored, they often fail to select an appropriate set of old containers for rewrite, generating a substantial number of invalid chunks in retrieved containers. To address this issue, we propose an inline deduplication approach for storage systems, called InDe , which uses a greedy algorithm to detect valid container utilization and dynamically adjusts the number of old container references in each segment. InDe fully leverages the distribution of duplicated chunks to improve the restore performance while maintaining high backup performance. We define an effectiveness metric, valid container referenced counts (VCRC) , to identify appropriate containers for the rewrite. We design a rewrite algorithm F-greedy that detects valid container utilization to rewrite low-VCRC containers. According to the VCRC distribution of containers, F-greedy dynamically adjusts the number of old container references to only share duplicate chunks with high-utilization containers for each segment, thereby improving the restore speed. To take full advantage of the above features, we further propose another rewrite algorithm called F-greedy+ based on adaptive interval detection of valid container utilization. F-greedy+ makes a more accurate estimation of the valid utilization of old containers by detecting trends of VCRC’s change in two directions and selecting referenced containers in the global scope. We quantitatively evaluate InDe using three real-world backup workloads. The experimental results show that compared with two state-of-the-art algorithms (Capping and SMR), our scheme improves the restore speed by 1.3×–2.4× while achieving almost the same backup performance.

Data deduplication has been widely used to improve storage space utilization, however, it is baffled by data fragmen-tation: logically consecutive chunks physically scattered across various containers. Many rewriting schemes, rewriting fragment-ed duplicate chunks into new containers, attempt to alleviate the restore performance degradation caused by fragmentation. Unfortunately, these schemes rely on a fixed threshold and fail to choose the appropriate set of old containers for rewriting, which leads to substantial redundant chunks existing in the retrieved containers when restoring backups. To address this issue, we propose a flexible threshold rewriting scheme to improve restore performance while maintaining high backup performance. We define an effectiveness metric - valid container reference counts (VCRC) - that facilitates identifying the appropriate containers for rewriting. We design a greedy-algorithm-based algorithm called F-greedy that dynamically adjusts the threshold according to the distribution of containers' VCRC, aiming to rewrite low-VCRC containers. We quantitatively evaluate F-greedy on three real-world backup datasets in terms of restore performance, backup performance, and storage overhead. The empirical results show that compared with two state-of-the-art schemes (Capping and SMR), our scheme improves the restore speed of the exiting algorithms by 1.3x - 2.4x while achieving similar backup performance.

Internal waves are a widespread geophysical phenomenon in stratified fluids and studying internal features in the coastal ocean is an important task. Using satellite imagery for studying oceanic internal waves is very popular and studying the low altitude aerial oblique photograph is a new direction. In this paper, we study the images captured from a circling aircraft which track a number of internal wave packets. The captured images are first rectified and photogram metrically mapped to ground coordinates, and then we use canny edge detector to find the internal wave propagation direction. The first several waves' ridges of each ground coordinated images are also exactly labeled and propagation speeds can be achieved. Experiment results show the performance of our algorithms.