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Flash-memory-based solid-state drives (SSDs) are used widely for secondary storage. To be effective for SSDs, traditional indices have to be redesigned to cope with the special properties of flash memory, such as asymmetric read/write latencies (fast reads and slow writes) and out-of-place updates. Previous flash-optimized indices focus mainly on reducing random writes to SSDs, which is typically accomplished at the expense of a substantial number of extra reads. However, modern SSDs show a narrowing gap between read and write speeds, and read operations on SSDs increasingly affect the overall performance of indices on SSDs. As a consequence, how to optimize SSD-aware indices by reducing both write and read costs is a pertinent and open challenge. We propose a new tree index for SSDs that is able to reduce both writes and extra reads. In particular, we use an update buffer and overflow pages to reduce random writes, and we further exploit Bloom filters to reduce the extra reads to the overflow nodes in the tree. With this mechanism, we construct a read/write-optimized index that is capable of offering better overall performance than previous flash-aware indices. In addition, we present an analysis of the proposed index and show that the read and write costs of the operations on the index can be balanced by only tuning the false-positive rate of the Bloom filters. Our experimental results suggest that our proposal is efficient and represents an improvement over existing methods. [ABSTRACT FROM AUTHOR]

Recently, with the widely use of flash memory based solid state drives (SSDs), a lot of studies have been conducted on SSD-based data management, such as index structures, query processing, and buffer management schemes. This paper focuses on buffer schemes for SSD-based database systems. However, differing from previous studies, we concentrate on buffer schemes for tree indexes on SSDs. This work is motivated by the observation that access patterns on index pages are much different from those on data pages. Generally, in a typical tree index, e.g., B+-tree, the root and internal nodes have higher read frequencies than leaf nodes have. However, traditional SSD-oriented buffering methods do not consider this special feature of indexes, and thus is not efficient when used as index buffer management schemes. In this paper, we present a new buffering scheme for tree indexes on SSDs that is named Clean-First and Dirty-Redundant-Write (CFDRW) scheme. The contributions of CFDRW are manifold. First, it assigns priorities to index pages to reflect the differences of access patterns of the nodes in a tree index. Second, it uses priority and recency to detect the hotness of index pages and proposes a new replacement algorithm based on priority and recency of the buffered index pages. Third, it exploits the internal parallelism of SSDs and proposes to write out buffer pages in a coarse granularity, i.e., to write out several pages using one physical I/O operation. We compare our proposal on two commodity SSDs with many previous methods including LRU, LIRS, and six flash-memory-based buffering schemes, by using synthetic and real workloads. The results show that our proposal outperforms the competitors under all workloads and SSDs in terms of various metrics including hit ratio, read count, write count, and elapsed time. [ABSTRACT FROM AUTHOR]

Flash-memory-based solid state drives (SSD) have been widely used in computer systems. Due to the high price and some specific features of SSD such as asymmetric read/write speeds and limited erasure endurance, it has been a very common solution, e.g., in modern data centers, to use hybrid storage systems involving SSD and traditional hard disks (HDD). However, the SSD/HDD-based hybrid storage systems introduce some new problems in the indexing schemes for data management. In this paper, we propose a new B+-tree-based index for such hybrid storage systems, which is called HybridB tree. The HybridB tree aims to reduce the random writes to SSD while keeping high time performance and low buffer costs. Particularly, we introduce a new design called huge leaf to avoid the splits and merges on B+-tree. A huge leaf node contains two or more leaf nodes in different states. We place the leaf nodes on HDD or SSD according to their current states, and dynamically adapt the states of leaf nodes when they are read or updated. After a detailed explanation on the structure and operations of the HybridB tree, we give a theoretical analysis on the costs of the HybridB tree. Then, we conduct experiments on two TPC-C traces, using a real hybrid storage system including one HDD and two SSDs, and compare the performance of our proposal with two implementations of B+-tree, namely the B+-tree on HDD and the B+-tree on SSD/HDD. The results show that our proposal has the best time performance and the fewest buffer costs. Moreover, our proposal is able to effectively reduce the random writes to SSD. [ABSTRACT FROM AUTHOR]

Memorizing the past information for later environments is an effective and widely employed approach to optimize dynamic problems. Although the existing explicit memories for dynamic optimization differ widely in the literature, all of them organize memory entries in a linear list. This naive structure leads to problems, such as heavy computational overhead and small memory capacity, and thus restricts the performance of the memories. In this paper, the binary space partition tree is adopted to organize the memory entries, and then a memory tree is constructed. The memory tree partitions the search space into regions. In order to make use of the memory tree, a neighbor shift strategy is proposed. When a new individual is generated in a region that has never been visited since the last change, the new individual is shifted to the neighboring memory individual of that region, if it is less fit than the memory individual. The proposed approach can be easily combined with many population-based algorithms for dynamic optimization in the real space. As examples, the proposed approach was combined with a basic particle swarm optimizer and two state-of-the-art dynamic optimizers. The experimental results showed that it significantly enhanced the performance of the three optimizers on various test problems. The proposed approach demonstrates the importance of memory structure in memory approaches. [ABSTRACT FROM AUTHOR]

Flash memory is widely used in embedded devices and enterprise storage systems. Currently, flash-based storage devices usually use a flash translation layer (FTL) to cope with the special features of flash memory. Many methods for the design and implementation of the FTL have been proposed, such as BAST (block-associative sector translation), FAST (fully associative sector translation), and IPL (inpage logging), of which IPL has been demonstrated to have the best performance. However, IPL offers little consideration to reducing merge operations that consequently result in the degradation of the overall performance of flash-memory storage systems. We propose an improvement to IPL, called adaptive IPL (AIPL). The idea of adaptive IPL is to make the log region in a block resizable, therefore a hot block (i.e., a write-intensive block) will use a large log region so as to absorb more page updates and in turn reduce the merge operations, while a cold block, i.e., a block rarely written to, will use a small log region. This is realized by first detecting the update pattern of a block and then presenting an updatepattern-based algorithm to dynamically adjust the log region size of a newly allocated block. We conduct experiments on TPC-C traces and synthetic traces and compare the performance of AIPL with other competitors in terms of merge count, write count and elapsed time. The results demonstrate that compared with IPL, AIPL can reduce merge operations by 65% and write operations by 54% on average. [ABSTRACT FROM AUTHOR]

Recently, negative databases (NDBs) are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor ( kNN) classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novelmethod for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising. [ABSTRACT FROM AUTHOR]

For a number of services with similar functionality reputation has been regarded as one of the most important methods to identify good ones from bad ones. However, a composite service, which is composed of multiple component services, obtains only one score (or feedback) after every invocation. In order to compute the reputation of each component service, it is necessary for the composite service to distribute this score to its component services. How to achieve a fair distribution is a challenging issue, as each component service may perform differently in contributing to the success or failure of the composite service. Although several efforts have been made for this problem, they do not consider the context of composition, which makes the distribution unfair. Therefore, in this paper, we propose a fair score distribution framework which combines the context of component services and their runtime performance. We distinguish two aspects contexts of a component service: structure-related importance and community-related replaceability, and adopt graph theory and dominating relationship technique to compute them, respectively. Experimental results show that our approach can achieve a more reasonable and fair score distribution than other existing methods. [ABSTRACT FROM AUTHOR]