File-search service is a valuable facility to accelerate many analytics applications, because it can drastically reduce the scale of the input data. The main challenge facing the design of large-scale and accurate file-search services is how to support real-time indexing in an efficient and scalable way. To address this challenge, we propose a distributed file-search service, called Propeller, which utilizes a special file-access pattern, called access-causality, to partition file-indices in order to expose substantial access locality and parallelism to accelerate the file-indexing process. The extensive evaluations of Propeller show that it is real-time in file-indexing operations, accurate in file-search results, and scalable in large datasets. It achieves significantly better file-indexing and file-search performance (up to 250x) than a centralized solution (MySQL) and much higher accuracy and substantially lower query latency (up to 22x than a state-of-the-art desktop search engine (Spotlight).

In personal computing devices that rely on a cloud storage environment for data backup, an imminent challenge facing source deduplication for cloud backup services is the low deduplication efficiency due to a combination of the resource-intensive nature of deduplication and the limited system resources. In this paper, we present ALG-Dedupe, an Application-aware Local-Global source deduplication scheme that improves data deduplication efficiency by exploiting application awareness, and further combines local and global duplicate detection to strike a good balance between cloud storage capacity saving and deduplication time reduction. We perform experiments via prototype implementation to demonstrate that our scheme can significantly improve deduplication efficiency over the state-of-the-art methods with low system overhead, resulting in shortened backup window, increased power efficiency and reduced cost for cloud backup services of personal storage.

The explosive growth in data volume and complexity imposes great challenges for file systems. To address these challenges, an innovative namespace management scheme is in desperate need to provide both the ease and efficiency of data access. In almost all today's file systems, the namespace management is based on hierarchical directory trees. This tree-based namespace scheme is prone to severe performance bottlenecks and often fails to provide real-time response to complex data lookups. This paper proposes a Semantic-Aware Namespace scheme, called SANE, which provides dynamic and adaptive namespace management for ultra-large storage systems with billions of files. SANE introduces a new naming methodology based on the notion of semantic-aware per-file namespace, which exploits semantic correlations among files, to dynamically aggregate correlated files into small, flat but readily manageable groups to achieve fast and accurate lookups. SANE is implemented as a middleware in conventional file systems and works orthogonally with hierarchical directory trees. The semantic correlations and file groups identified in SANE can also be used to facilitate file prefetching and data de-duplication, among other system-level optimizations. Extensive trace-driven experiments on our prototype implementation validate the efficacy and efficiency of SANE.

Previous on-board RAM management algorithms of SSD are oblivious of garbage collection (GC) efficiency in that they fail to analyze the performance degradation from the perspective of GC efficiency and thus unable to find the true culprit. For some workloads that do not generate enough invalid data, these approaches will suffer from severe performance degradation. Further, the existing write-buffer component in SSD is by and large oblivious of how FTL works in SSD, unable to minimize the address translation overhead. The GC efficiency and FTL obliviousness of SSD RAM buffer management can result in significantly increased GC-induced and address-translation-induced write traffic to the flash memory, which is very harmful to both performance and endurance. To address these performance and endurance issues of SSD, we propose GC-ARM, an SSD RAM management scheme consisting of a write buffer component and an FTL component. On the one hand, the write-buffer design of GC-ARM can improve the garbage collection efficiency by evaluating the benefits of different destaging approaches. On the other hand, the FTL component is designed to interact with the write-buffer component to reduce the address translation overhead. Moreover, GC-ARM optimizes the size ratio of RAM space allocated to the write buffer and the FTL's mapping table based on the varying randomness of workloads. Extensive trace-driven evaluation results show that GC-ARM consistently outperforms the state-of-the-art FTL and buffer management schemes in terms of the number of erasures, average response time, garbage collection efficiency and write traffic reduction.

Flash memory SSDs pose a well-known challenge, that is, the erase-before-write problem. Researchers try to solve this inherent problem from two different angles by either designing sophisticated Flash Translation Layer (FTL) schemes to postpone and minimize erasures or designing flash-aware buffer management algorithms to absorb unnecessary erasures. Our experimental results show that buffer management inside SSD is necessary and indispensable. Further, based on our observation that TPC and some server workloads have strong temporal locality, this paper proposes a new flash-aware write-buffer management algorithm, called PUD-aware LRU algorithm (PUD-LRU), based on the Predicted average Update Distance (PUD) as the key block replacement criterion on top of log-block FTL schemes. The main idea of PUD-LRU is to differentiate blocks and judiciously destage blocks based on their frequency and recency so as to avoid the unnecessary erasures due to repetitive updates. To take advantage of the characteristics of log-block FTL and increase the erase efficiency, PUD-LRU maximizes the number of valid pages in the destaged block in each erase operation. Our trace-driven experimental results show that the PUD-LRU algorithm can reduce the number of erasures of the state-of-the-art buffer management algorithm BPLRU by up to 42%, and average response time by up to 56%, while reducing the two measures of the state-of-the-art page-mapping FTL scheme DFTL by up to 73% and 75%, respectively.