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.
, @cite_16 and , @cite_11 proposed models to use the MEMS storage device just like a disk. They abstract the MEMS storage device as a linear array of fixed-size logical blocks with one head. This linear abstraction works well for most applications using the MEMS storage device as the replacement of the disk , @cite_16 . However, they provide relatively poor data retrieval performance compared with device-specific approaches , @cite_0 @cite_2 because they do not take full advantage of the characteristics of the MEMS storage device , @cite_13 .
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Due to the large difference between seek time and transfer time in current disk technology, it is advantageous to perform large I O using a single sequential access rather than multiple small random I O accesses. However, prior optimal cost and data placement approaches for processing range queries over two-dimensional datasets do not consider this property. In particular, these techniques do not consider the issue of sequential data placement when multiple I O blocks need to be retrieved from a single device. In this paper, we reevaluate the optimal cost of range queries by declustering two-dimensional datasets over multiple devices, and prove that, in general, it is impossible to achieve the new optimal cost. This is because disks cannot facilitate two-dimensional sequential access which is required by the new optimal cost. Then we revisit the existing data allocation schemes under the new optimal cost, and show that none of them can achieve the new optimal cost. Fortunately, MEMS-based storage is being developed to reduce I O cost. We first show that the two-dimensional sequential access requirement can not be satisfied by simply modeling MEMS-based storage as conventional disks. Then we propose a new placement scheme that exploits the physical properties of MEMS-based storage to solve this problem. Our theoretical analysis and experimental results show that the new scheme achieves almost optimal I O costs. 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. 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. MEMS-based storage devices (MEMStores) are significantly different from both disk drives and semiconductor memories. The differences motivate the question of whether they need new abstractions to be utilized by systems, or if existing abstractions will be sufficient. This paper addresses this question by examining the fundamental reasons that the abstraction works for existing devices, and by showing that these reasons also hold for MEMStores. This result is shown to hold through several case studies of proposed roles MEMStores may take in future systems and potential policies that may be used to tailor systems' access to MEMStores. With one noted exception, today's storage interfaces and abstractions are as suitable for MEMStores as for disks. Probe-based storage, also known as micro-electric mechanical systems (MEMS) storage, is a new technology that is emerging to bypass the fundamental limitations of disk drives. The design space of such devices is particularly interesting because we can architect these devices to different design points, each with different performance characteristics. This makes it more difficult to understand how to use probe-based storage in a system. Although researchers have modeled access times and simulated performance of workloads, such simulations are time-intensive and make it difficult to exhaustively search the parameter space for optimal configurations. To address this problem, we have created a parameterized analytical model that computes the average request latency of a probe-based storage device. Our error compared to a simulated device using real-world traces is small (less than 15 for service time). With this model we can identify configurations that will satisfy specific performance objectives, greatly narrowing the search space of configurations one must simulate.