Workload-Based Configuration of MEMS-Based Storage Devices for Mobile Systems

Because of its small form factor, high capacity, and expected low cost, MEMS-based storage is a suitable storage technology for mobile systems. However, flash memory may outperform MEMS-based storage in terms of performance, and energy-efficiency. The problem is that MEMS-based storage devices have a large number (i.e., thousands) of heads, and to deliver peak performance, all heads must be deployed simultaneously to access each single sector. Since these devices are mechanical and thus some housekeeping information is needed for each head, this results in a huge capacity loss and increases the energy consumption of MEMS-based storage with respect to flash. We solve this problem by proposing new techniques to lay out data in MEMS-based storage devices. Data layouts represent optimizations in a design space spanned by three parameters: the number of active heads, sector parallelism, and sector size. We explore this design space and show that by exploiting knowledge of the expected workload, MEMS-based devices can employ all heads, thus delivering peak performance, while decreasing the energy consumption and compromising only a little on the capacity. Our exploration shows that MEMS-based storage is competitive with flash in most cases, and outperforms flash in a few cases

A Logical Model and Data Placement Strategies for MEMS Storage Devices

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. 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 and derive new data placements for relational data and two-dimensional spatial data by using those strategies. Experimental results show that the proposed data placements improve the data retrieval performance by up to 4.0 times for relational data and by up to 4.8 times for two-dimensional spatial data of approximately 320 Mbytes compared with those of existing data placements. Further, these improvements are expected to be more marked as the database size grows.Comment: 37 page

Memory Deduplication: An Effective Approach to Improve the Memory System

Programs now have more aggressive demands of memory to hold their data than before. This paper analyzes the characteristics of memory data by using seven real memory traces. It observes that there are a large volume of memory pages with identical contents contained in the traces. Furthermore, the unique memory content accessed are much less than the unique memory address accessed. This is incurred by the traditional address-based cache replacement algorithms that replace memory pages by checking the addresses rather than the contents of those pages, thus resulting in many identical memory contents with different addresses stored in the memory. For example, in the same file system, opening two identical files stored in different directories, or opening two similar files that share a certain amount of contents in the same directory, will result in identical data blocks stored in the cache due to the traditional address-based cache replacement algorithms. Based on the observations, this paper evaluates memory compression and memory deduplication. As expected, memory deduplication greatly outperforms memory compression. For example, the best deduplication ratio is 4.6 times higher than the best compression ratio. The deduplication time and restore time are 121 times and 427 times faster than the compression time and decompression time, respectively. The experimental results in this paper should be able to offer useful insights for designing systems that require abundant memory to improve the system performance

Memory Deduplication: An Effective Approach to Improve the Memory System

Programs now have more aggressive demands of memory to hold their data than before. This paper analyzes the characteristics of memory data by using seven real memory traces. It observes that there are a large volume of memory pages with identical contents contained in the traces. Furthermore, the unique memory content accessed are much less than the unique memory address accessed. This is incurred by the traditional address-based cache replacement algorithms that replace memory pages by checking the addresses rather than the contents of those pages, thus resulting in many identical memory contents with different addresses stored in the memory. For example, in the same file system, opening two identical files stored in different directories, or opening two similar files that share a certain amount of contents in the same directory, will result in identical data blocks stored in the cache due to the traditional address-based cache replacement algorithms. Based on the observations, this paper evaluates memory compression and memory deduplication. As expected, memory deduplication greatly outperforms memory compression. For example, the best deduplication ratio is 4.6 times higher than the best compression ratio. The deduplication time and restore time are 121 times and 427 times faster than the compression time and decompression time, respectively. The experimental results in this paper should be able to offer useful insights for designing systems that require abundant memory to improve the system performance

Designing Computer Systems with MEMS-based Storage

For decades the RAM-to-disk memory hierarchy gap has plagued computer architects. An exciting new storage technology based on microelectromechanical systems (MEMS) is poised to fill a large portion of this performance gap, significantly reduce system power consumption, and enable many new applications. This paper explores the system-level implications of integrating MEMS-based storage into the memory hierarchy. Results show that standalone MEMS-based storage reduces I/O stall times by 4-74X over disks and improves overall application runtimes by 1.9-4.4X. When used as on-board caches for disks, MEMS-based storage improves I/O response time by up to 3.5X. Further, the energy consumption of MEMS-based storage is 10-54X less than that of state-of-the-art low-power disk drives. The combination of the high-level physical characteristics of MEMS-based storage (small footprints, high shock tolerance) and the ability to directly integrate MEMS-based storage with processing leads to such new ap..

Designing computer systems with MEMS-based storage

Abstract: "For decades the RAM-to-disk memory hierarchy gap has plagued computer architects. An exciting new storage technology based on microelectromechanical systems (MEMS) is poised to fill a large portion of this performance gap, significantly reduce power consumption, and enable many new classes of applications. This research explores the impact that several different MEMS-based storage designs will have on computer systems. Results from five application studies show these devices reduce application I/O stall times by 3-10X and improve overall application performance by 1.6-8.1X. Further, integrating MEMS-based storage as a disk cache achieves a 3.5X performance improvement over a standalone disk drive. Power consumption simulations show that MEMS-based storage devices use up to 10X less power than state-of-the-art low-power disk drives. Many of these improvements stem from the fact that average access times for MEMS-based storage are 10X faster than disks and that MEMS devices are able to rapidly move between active and power-down mode. Combined with the differences in the physical behavior of MEMS-based storage, these characteristics create numerous opportunities for restructuring the storage/memory hierarchy.

Designing Computer Systems with MEMS-based Storage (CMU-CS-00-137)

For decades the RAM-to-disk memory hierarchy gap has plagued computer architects. An exciting new storage technology based on microelectromechanical systems (MEMS) is poised to fill a large portion of this performance gap, significantly reduce system power consumption, and enable many new applications. This paper explores the system-level implications of integrating MEMS-based storage into the memory hierarchy. Results show that standalone MEMS-based storage reduces I/O stall times by 4-74X over disks and improves overall application runtimes by 1.9-4.4X. When used as on-board caches for disks, MEMS-based storage improves I/O response time by up to 3.5X. Further, the energy consumption of MEMS-based storage is 10-54X less than that of state-of-the-art low-power disk drives. The combination of the high-level physical characteristics of MEMS-based storage (small footprints, high shock tolerance) and the ability to directly integrate MEMS-based storage with processing leads to such new applications as portable gigabit storage systems and ubiquitous active storage nodes