Jun-seok Shim

DIG: Rapid Characterization of Modern Hard Disk Drive and Its Performance Implication

In this work, we develop novel disk characterization suite, DIG (disk geometry analyzer), which allows us to rapidly extract and to characterize the key performance metric of modern hard disk drive. Development of this tool is accompanied by thorough examination of four off-the-shelf hard disk drives. DIG consists of three key ingredients: O(1) track boundary detection algorithm,O (log n) zone boundary detection algorithm, and hybrid sampling based seek time profiling. We particularly focus on addressing the scalability aspect of disk characterization. With DIG, we are able to extract key metrics of hard disk drive within 3-20 min. DIG allows us to determine the sector layout mechanism of the underlying hard disk drive, e.g. hybrid serpentine, cylinder serpentine and surface serpentine, and to build complete sector map from LBN to three dimensional space of (cylinder, head, sector). Examining the disks with DIG, we found a number of important observations. Modern hard disk drive puts great emphasis on minimizing the head switch overhead. This is done via sector layout mechanism and and surface serpentine and hybrid serpentine is the typical way of avoiding it. Legacy disk seek time model leaves much to be desired to be used in modern hard disk drive especially in short seeks (less than 5000 tracks).

Intelligent storage: Cross-layer optimization for soft real-time workload

In this work, we develop an intelligent storage system framework for soft real-time applications. Modern software systems consist of a collection of layers and information exchange across the layers is performed via well-defined interfaces. Due to the strictness and inflexibility of interface definition, it is not possible to pass the information specific to one layer to other layers. In practice, the exploitation of this information across the layers can greatly enhance the performance, reliability, and manageability of the system. We address the limitation of legacy interface definition via enabling intelligence in the storage system. The objective is to enable the lower-layer entity, for example, a physical or block device, to conjecture the semantic and contextual information of that application behavior which cannot be passed via the legacy interface. Based upon the knowledge obtained by the intelligence module, the system can perform a number of actions to improve the performance, reliability, security, and manageability of the system. Our intelligence storage system focuses on optimizing the I/O subsystem performance for a soft real-time application. Our intelligence framework consists of three components: the workload monitor, workload analyzer, and system optimizer. The workload monitor maintains a window of recent I/O requests and extracts feature vectors in regular intervals. The workload analyzer is trained to determine the class of the incoming workload by using the feature vector. The system optimizer performs various actions to tune the storage system for a given workload. We use confidence rate boosting to train the workload analyzer. This sophisticated learner achieves a higher than 97% accuracy of workload class prediction. We develop a prototype intelligence storage system on the legacy operating system platform. The system optimizer performs; (1) dynamic adjustment of the file-system-level read-ahead size; (2) dynamic adjustment of I/O request size; and (3) filtering of I/O requests. We examine the effect of this autonomic optimization via experimentation. We find that the storage level pro-active optimization greatly enhances the efficiency of the underlying storage system. The sophisticated intelligence module developed in this work does not restrict its usage for performance optimization. It can be effectively used as classification engine for generic autonomic computing environment, i.e. management, diagnosis, security and etc.

A new seek servo controller for minimizing power consumption in micro hard disk drives

Recently, the demand for micro hard disk drive that provides high-capacity removable storage for handheld electronic devices is increasing very rapidly. The major concern in the design of seek servo controller in micro disk drives is to reduce power consumption. The input power delivered to the seek servo system is consumed by the transistors of power amplifier and motor coil resistance. In this paper, we present a new seek servo controller for minimizing the power consumption. We use a Fourier decomposition and constrained nonlinear programming to determine the optimum seek profile that minimizes the power consumption. Finally, we present some experimental results using a commercially available micro disk drive.

Hard Disk Drive for HD Quality Multimedia Home Appliance

In this work, we develop a novel audio and video (A/V) disk which is not only stronger for fragmentation but also getting higher bandwidth for multimedia home appliances. For our work, multimedia workload characteristics and disk overheads in multimedia home appliance are analyzed and then the concept of extent is redefined. If track size is only multiple size of extents, every I/O operation do not have track switch overhead. We suggest three ways of implementing our model in a real disk drive and merits and demerits of each scheme are analyzed. Disk performance can show various patterns according to input workloads generated by filesystem. Designing fragmentation model is based on EXT3 filesystem and Disksim 3.0 are used for the simulation based performance evaluation. A state-of-the-art disk layout is added and many parameters are modified to reflect actual response time pattern of real disk in Disksim. Through our experiments, our model shows a significant improvement in bandwidth from 5% to 25% fragmentation ratio.

Free Fall Detection Algotithms for Hard Disk Drives

For mobile HDD, shock resistance is one of the main parameters to compare with other storage devices. One way to improve this parameter is by detecting the free fall of the consumer device and take protective measures before the impact with the ground. The new approach aims to improve the reliability of free-fall detection algorithm in broad range of real-life situations like jogging, running, dancing. The conventional approach is to measure the acceleration using the acceleration sensor and react based on acceleration threshold with timing reaction. The issue is false positive alarms, namely the detection of free fall, while there is no actual free fall. The proposed new approach is to integrate the measured acceleration to obtain the area between the acceleration threshold and the current acceleration measurement below the threshold. The detection of the free fall is performed not only depending on the acceleration, but also depending on the calculated area value