A ’Millipede’ scanner model - Energy consumption and performance

This short report (1) describes an energy model for the seek and read/write operations in a mass-balanced Y-scanner for parallel-probe storage by IBM [1] and (2) updates the settings of the MEMS model in DiskSim with recent published figures from this XY-scanner. To speedup system simulations, a straight forward second-order model is used without control loop. Read/write operation is modeled by quasi-static calculations. To approximate seek behavior, ’bang-bang’ control is assumed; the result is close to the actual behavior with control loop [2]. Unfortunately, no energy measurements were available to validate the model. Using the proposed energy model, we are able to study the energy consumption of a MEMS-based storage device for different application areas and file systems

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

Shutdown Policies for MEMS-Based Storage Devices -- Analytical Models

MEMS-based storage devices should be energy ecient for deployment in mobile systems. Since MEMS-based storage devices have a moving me- dia sled, they should be shut down during periods of inactivity. However, shutdown costs energy, limiting the applicability of aggressive shutdown decisions. The media sled in MEMS-based storage devices is suspended by springs. We introduce a policy that exploits the spring structure to reduce the shut- down energy. As a result, the aggressiveness of the shutdown decisions can be increased, reducing the energy consumption. This report devises analytical models of the shutdown time and energy of this policy

Physical Modeling of Probe-Based Storage

Magnetic disks may be reaching physical performance limits due to the superparamagnetic effect. To close the performance gap between processors and storage, researchers are exploring a variety of new storage technologies [17]. Among these new technologies, probe-based micro-electrical mechanical systems (MEMS) magnetic storage arrays are attractive [3]. Probe-based storage is dense and highly parallel

Physical Modeling of Probe-Based Storage

Magnetic disks may be reaching physical performance limits due to the superparamagnetic effect. To close the performance gap between processors and storage, researchers are exploring a variety of new storage technologies [17]. Among these new technologies, probe-based micro-electrical mechanical systems (MEMS) magnetic storage arrays are attractive [3]. Probe-based storage is dense and highly parallel. It uses rectilinear motion in contrast to rotating media. Commercial devices are expected within the next several years. The wide range of possible architectures and the unique performance characteristics of probe-based storage require that standard file system algorithms for disks, including scheduling and layout, must be revisited to determine their efficiency domain. Because these devices do not yet exist, analysis of system performance depends on simulation models. At this early stage of development, models that bridge the gap between the physics of the device and its performance characteristics can provide important feedback to both hardware and software designers. This paper compares results from three models of probe-based storage that convey successively more accurate descriptions of the underlying physics. We conclude that the physical accuracy of the model has a significant impact on the predicted performance under real workloads.