You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In recent years, information retrieval algorithms have taken center stage for extracting important data in ever larger datasets. Advances in hardware technology have lead to the increasingly wide spread use of flash storage devices. Such devices have clear benefits over traditional hard drives in terms of latency of access, bandwidth and random access capabilities particularly when reading data. There are however some interesting trade-offs to consider when leveraging the advanced features of such devices. On a relative scale writing to such devices can be expensive. This is because typical flash devices (NAND technology) are updated in blocks. A minor update to a given block requires the entire block to be erased, followed by a re-writing of the block. On the other hand, sequential writes can be two orders of magnitude faster than random writes. In addition, random writes are degrading to the life of the flash drive, since each block can support only a limited number of erasures. TF-IDF can be implemented using a counting hash table. In general, hash tables are a particularly challenging case for the flash drive because this data structure is inherently dependent upon the randomness of the hash function, as opposed to the spatial locality of the data. This makes it difficult to avoid the random writes incurred during the construction of the counting hash table for TF-IDF. In this paper, we will study the design landscape for the development of a hash table for flash storage devices. We demonstrate how to effectively design a hash table with two related hash functions, one of which exhibits a data placement property with respect to the other. Specifically, we focus on three designs based on this general philosophy and evaluate the trade-offs among them along the axes of query performance, insert and update times and I O time through an implementation of the TF-IDF algorithm.

Cited Abstract(s):
[1]: Large flash disks, or solid state drives (SSDs), have become an attractive alternative to magnetic hard disks, due to their high random read performance, low energy consumption and other features. However, writes, especially small random writes, on flash disks are inherently much slower than reads because of the erase-before-write mechanism. To address this asymmetry of read-write speeds in tree indexing on the flash disk, we propose FD-tree, a tree index designed with the logarithmic method and fractional cascading techniques. With the logarithmic method, an FD-tree consists of the head tree -- a small B+-tree on the top, and a few levels of sorted runs of increasing sizes at the bottom. This design is write-optimized for the flash disk; in particular, an index search will potentially go through more levels or visit more nodes, but random writes are limited to a small area -- the head tree, and are subsequently transformed into sequential ones through merging into the lower runs. With the fractional cascading technique, we store pointers, called fences, in lower level runs to speed up the search. Given an FD-tree of n entries, we analytically show that it performs an update in O(logB n) sequential I Os and completes a search in O(logB n) random I Os, where B is the flash page size. We evaluate FD-tree in comparison with representative B+-tree variants under a variety of workloads on three commodity flash SSDs. Our results show that FD-tree has a similar search performance to the standard B+-tree, and a similar update performance to the write-optimized B+-tree variant. As a result, FD-tree dominates the other B+-tree index variants on the overall performance on flash disks as well as on magnetic disks.

[2]: We present FlashStore, a high throughput persistent key-value store, that uses flash memory as a non-volatile cache between RAM and hard disk. FlashStore is designed to store the working set of key-value pairs on flash and use one flash read per key lookup. As the working set changes over time, space is made for the current working set by destaging recently unused key-value pairs to hard disk and recycling pages in the flash store. FlashStore organizes key-value pairs in a log-structure on flash to exploit faster sequential write performance. It uses an in-memory hash table to index them, with hash collisions resolved by a variant of cuckoo hashing. The in-memory hash table stores compact key signatures instead of full keys so as to strike tradeoffs between RAM usage and false flash read operations. FlashStore can be used as a high throughput persistent key-value storage layer for a broad range of server class applications. We compare FlashStore with BerkeleyDB, an embedded key-value store application, running on hard disk and flash separately, so as to bring out the performance gain of FlashStore in not only using flash as a cache above hard disk but also in its use of flash aware algorithms. We use real-world data traces from two data center applications, namely, Xbox LIVE Primetime online multi-player game and inline storage deduplication, to drive and evaluate the design of FlashStore on traditional and low power server platforms. FlashStore outperforms BerkeleyDB by up to 60x on throughput (ops sec), up to 50x on energy efficiency (ops Joule), and up to 85x on cost efficiency (ops sec dollar) on the evaluated datasets.

[3]: We show how to build cheap and large CAMs, or CLAMs, using a combination of DRAM and flash memory. These are targeted at emerging data-intensive networked systems that require massive hash tables running into a hundred GB or more, with items being inserted, updated and looked up at a rapid rate. For such systems, using DRAM to maintain hash tables is quite expensive, while on-disk approaches are too slow. In contrast, CLAMs cost nearly the same as using existing on-disk approaches but offer orders of magnitude better performance. Our design leverages an efficient flash-oriented data-structure called BufferHash that significantly lowers the amortized cost of random hash insertions and updates on flash. BufferHash also supports flexible CLAM eviction policies. We prototype CLAMs using SSDs from two different vendors. We find that they can offer average insert and lookup latencies of 0.006ms and 0.06ms (for a 40 lookup success rate), respectively. We show that using our CLAM prototype significantly improves the speed and effectiveness of WAN optimizers.

[4]: 

[5]: This paper presents a new cluster architecture for low-power data-intensive computing. FAWN couples low-power embedded CPUs to small amounts of local flash storage, and balances computation and I O capabilities to enable efficient, massively parallel access to data. The key contributions of this paper are the principles of the FAWN architecture and the design and implementation of FAWN-KV--a consistent, replicated, highly available, and high-performance key-value storage system built on a FAWN prototype. Our design centers around purely log-structured datastores that provide the basis for high performance on flash storage, as well as for replication and consistency obtained using chain replication on a consistent hashing ring. Our evaluation demonstrates that FAWN clusters can handle roughly 350 key-value queries per Joule of energy--two orders of magnitude more than a disk-based system.

[6]: Storage deduplication has received recent interest in the research community. In scenarios where the backup process has to complete within short time windows, inline deduplication can help to achieve higher backup throughput. In such systems, the method of identifying duplicate data, using disk-based indexes on chunk hashes, can create throughput bottlenecks due to disk I Os involved in index lookups. RAM prefetching and bloom-filter based techniques used by [42] can avoid disk I Os on close to 99 of the index lookups. Even at this reduced rate, an index lookup going to disk contributes about 0.1msec to the average lookup time - this is about 1000 times slower than a lookup hitting in RAM. We propose to reduce the penalty of index lookup misses in RAM by orders of magnitude by serving such lookups from a flash-based index, thereby, increasing inline deduplication throughput. Flash memory can reduce the huge gap between RAM and hard disk in terms of both cost and access times and is a suitable choice for this application. To this end, we design a flash-assisted inline deduplication system using ChunkStash, a chunk metadata store on flash. ChunkStash uses one flash read per chunk lookup and works in concert with RAM prefetching strategies. It organizes chunk metadata in a log-structure on flash to exploit fast sequential writes. It uses an inmemory hash table to index them, with hash collisions resolved by a variant of cuckoo hashing. The in-memory hash table stores (2-byte) compact key signatures instead of full chunk-ids (20-byte SHA-1 hashes) so as to strike tradeoffs between RAM usage and false flash reads. Further, by indexing a small fraction of chunks per container, ChunkStash can reduce RAM usage significantly with negligible loss in deduplication quality. Evaluations using real-world enterprise backup datasets show that ChunkStash outperforms a hard disk index based inline deduplication system by 7x-60x on the metric of backup throughput (MB sec).
There have been hash tables designed with SSDs including the work presented in [3] in the context of data intensive networked systems, [5] in the context of wimpy nodes, [6] is in the context of data de-duplication, [4] energy efficient memory sensors, and [2] persistent storage as write and or read caches. In [1] designs a tree index for flash devices. However, [1] does not address duplicate keys thus it cannot handle a counting hash.