Modeling and performance of MEMS-based storage devices
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Designing computer systems with MEMS-based storage
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Physical Modeling of Probe-Based Storage
MSS '01 Proceedings of the Eighteenth IEEE Symposium on Mass Storage Systems and Technologies
Using mems-based storage devices in computer systems
Using mems-based storage devices in computer systems
Awarded Best Paper! - Using MEMS-Based Storage in Disk Arrays
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Using MEMS-based storage in computer systems---MEMS storage architectures
ACM Transactions on Storage (TOS)
Using MEMS-based storage in computer systems---device modeling and management
ACM Transactions on Storage (TOS)
Migration-Resistant Policies for Probe-Wear Leveling in MEMS Storage Devices
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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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.