MEMS storage devices are new non-volatile secondary storages that have outstanding advantages over magnetic disks. MEMS storage devices, however, are much different from magnetic disks in the structure and access characteristics in the following ways. They have thousands of heads called probe tips and provide the following two major access facilities: (1) flexibility: freely selecting a set of probe tips for accessing data, (2) parallelism: simultaneously reading and writing data with the set of probe tips selected. Due to these characteristics, it is nontrivial to find data placements that fully utilize the capability of MEMS storage devices. In this paper, we propose a simple logical model called the Region-Sector (RS) model that abstracts major characteristics affecting data retrieval performance, such as flexibility and parallelism, from the physical MEMS storage model. We also suggest heuristic data placement strategies based on the RS model. To show the usability of the RS model, we derive new data placements for relational data and two-dimensional spatial data by using these strategies. Experimental results show that the proposed data placements improve the data retrieval performance by up to 4.7times for relational data and by up to 18.7times for two-dimensional spatial data of approximately 320Mbytes compared with those of existing data placements. Further, these improvements are expected to be more marked as the database size grows.
There have been a number of studies on data placement for the MEMS storage device. We classify them into two categories -- disk mapping approaches and device-specific approaches -- depending on whether they take advantage of the characteristics of the storage device. This classification of the MEMS storage device is analogous to that of the flash memory , @cite_4 , which is another type of new non-volatile secondary storage. For the flash memory, device-specific approaches ,(e.g., Yet Another Flash File System ,( YAFFS ) , @cite_9 ) provide new mechanisms to exploit the features of the flash memory in order to improve performance, while disk mapping approaches(e.g., Flash Translation Layer ,( FTL ) , @cite_18 ) abstract the flash memory as a linear array of fixed-size pages in order to use existing disk-based algorithms on the flash memory. In this section, we explain two categories for the MEMS storage device in more detail.
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MEMS-based storage devices promise significant performance, reliability, and power improvements relative to disk drives. This paper compares and contrasts these two storage technologies and explores how the physical characteristics of MEMS-based storage devices change four aspects of operating system (OS) management: request scheduling, data placement, failure management, and power conservation. Straightforward adaptations of existing disk request scheduling algorithms are found to be appropriate for MEMS-based storage devices. A new bipartite data placement scheme is shown to better match these devices' novel mechanical positioning characteristics. With aggressive internal redundancy, MEMS-based storage devices can mask and tolerate failure modes that halt operation or cause data loss for disks. In addition, MEMS-based storage devices simplify power management because the devices can be stopped and started rapidly. Recent advances in flash media have made it an attractive alternative for data storage in a wide spectrum of computing devices, such as embedded sensors, mobile phones, PDA's, laptops, and even servers. However, flash media has many unique characteristics that make existing data management analytics algorithms designed for magnetic disks perform poorly with flash storage. For example, while random (page) reads are as fast as sequential reads, random (page) writes and in-place data updates are orders of magnitude slower than sequential writes. In this paper, we consider an important fundamental problem that would seem to be particularly challenging for flash storage: efficiently maintaining a very large (100 MBs or more) random sample of a data stream (e.g., of sensor readings). First, we show that previous algorithms such as reservoir sampling and geometric file are not readily adapted to flash. Second, we propose B-FILE, an energy-efficient abstraction for flash media to store self-expiring items, and show how a B-FILE can be used to efficiently maintain a large sample in flash. Our solution is simple, has a small (RAM) memory footprint, and is designed to cope with flash constraints in order to reduce latency and energy consumption. Third, we provide techniques to maintain biased samples with a B-FILE and to query the large sample stored in a B-FILE for a subsample of an arbitrary size. Finally, we present an evaluation with flash media that shows our techniques are several orders of magnitude faster and more energy-efficient than (flash-friendly versions of) reservoir sampling and geometric file. A key finding of our study, of potential use to many flash algorithms beyond sampling, is that "semi-random" writes (as defined in the paper) on flash cards are over two orders of magnitude faster and more energy-efficient than random writes. Due to recent advances in semiconductor manufacturing, the gap between main memory and disks is constantly increasing. This leads to a significant performance bottleneck for Relational Database Management Systems. Recent advances in nanotechnology have led to the invention of MicroElectroMechanical Systems (MEMS) based storage technology to replace disks. In this paper, we exploit the physical characteristics of MEMS-based storage devices to develop a placement scheme for relational data that enables retrieval in both row-wise and column-wise manner. We develop algorithms for different relational operations based on this data layout. Our experimental results and analysis demonstrate that this data layout not only improves I O utilization, but results in better cache performance for a variety of different relational operations. Flash memory is among the top choices for storage media in ubiquitous computing. With a strong demand of high-capacity storage devices, the usages of flash memory quickly grow beyond their original designs. The very distinct characteristics of flash memory introduce serious challenges to engineers in resolving the quick degradation of system performance and the huge demand of main-memory space for flash-memory management when high-capacity flash memory is considered. Although some brute-force solutions could be taken, such as the enlarging of management granularity for flash memory, we showed that little advantage is received when system performance is considered. This paper proposes a flexible management scheme for large-scale flash-memory storage systems. The objective is to efficiently manage high-capacity flash-memory storage systems based on the behaviors of realistic access patterns. The proposed scheme could significantly reduce the main-memory usages without noticeable performance degradation.