Probes (read/write heads) in a MEMS storage device are susceptible to wear. We study probe wear, and analyze the causes of uneven wear. We show that under real-world workloads some probes can wear one order of magnitude faster than others. This premature expiry has severe consequences for reliability, timing performance, energy efficiency, and lifetime. Wear leveling precludes premature expiry and is thus necessary. We discuss the fundamental differences between probe wear in MEMS storage and medium wear in Flash, calling for a different treatment. We devise three policies to level probe wear. The policies provide a spectrum between best lifetime and least influence on the response time and energy efficiency of a MEMS storage device. We make the case that data migration can be prevented by augmenting the policies with a simple rule. We study the influence of the data layout configuration on the leveling performance of the policies.

Emerging nanotechnology-based systems encounter new non-functional requirements. This work addresses MEMS storage, an emerging technology that promises ultrahigh density and energy-efficient storage devices. We study the buffering requirement of MEMS storage in streaming applications. We show that capacity and lifetime of a MEMS device dictate the buffer size most of the time. Our study shows that trading off 10% of the optimal energy saving of a MEMS device reduces its buffer capacity by up to three orders of magnitude.

MEMS storage technology promises appealing features, such as ultrahigh density and low cost. To stay competitive, however, its performance must improve to fulfil the increasing I/O requirements in first-tier applications. Further, it must be energy-efficient. The maturity of MEMS techniques invites for a change in the basic single-medium architecture of MEMS storage. We propose three MEMS architectures of multiple media sleds. The three architectures vary in their performance and cost, and consume 35% less energy than the basic single-sled architecture of MEMS storage. We compare their timing performance in mobile, streaming, and database applications, and study their performance scalability.

An emerging storage technology, called MEMS-based storage, promises nonvolatile storage devices with ultrahigh density, high rigidity, a small form factor, and low cost. For these reasons, MEMS-based storage devices are suitable for battery-powered mobile systems such as PDAs. For deployment in such systems, MEMS-based storage devices must consume little energy. This work mainly targets reducing the energy consumption of this class of devices. We derive the operation modes of a MEMS-based storage device and systemically devise a policy in each mode for energy saving. Three types of policies are presented: power management, shutdown, and data-layout policy. Combined, these policies reduce the total energy consumed by a MEMS-based storage device. A MEMS-based storage device that enforces these policies comes close to Flash with respect to energy consumption and response time. However, enhancement on the device level is still needed; we present some suggestions to resolve this issue.

Probes (or read/write heads) in MEMS-based storage devices are susceptible to wear. We study probe wear, and analyze the causes of probe uneven wear. We show that under real-world traces some probes can wear one order of magnitude faster than other probes leading to premature expiry of some probes. Premature expiry has severe consequences for the reliability, timing performance, energy-efficiency, and the lifetime of MEMS-based storage devices. Therefore, wear-leveling is a must to preclude premature expiry. We discuss how probe wear in MEMS-based storage is different from medium wear in Flash, calling for a different treatment. We present three policies to level probe wear. By simulation against three real-world traces, our work shows that an inevitable trade-off exists between lifetime, timing performance, and energy efficiency. The policies differ in the size of the trade-off. One of the policies maximizes the lifetime, so that it is optimal; and the other two are less optimal, and are used based on the configuration of the device.

Because of its small form factor, high capacity, and expected low cost, MEMS-based storage is a suitable storage technology for mobile systems. MEMS-based storage devices should also be energy efficient for deployment in mobile systems. The problem is that MEMS-based storage devices are mechanical, and thus consume a large amount of energy when idle. Therefore, a power management (PM) policy is needed that maximizes energy saving while minimizing performance degradation. In this work, we quantitatively demonstrate the optimality of the fixed-timeout PM policy for MEMS-based storage devices. Because the media sled is suspended by springs across the head array in MEMS-based storage devices, we show that these devices (1) lack mechanical startup overhead and (2) exhibit small shutdown overhead. As a result, we show that the combination of a PM policy, that fixes the timeout in the range of 1--10~ms, and a shutdown policy, that exploits the springs, results in a near-optimal energy saving yet at a negligible loss in performance.

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.