Li-Pin Chang

An adaptive striping architecture for flash memory storage systems of embedded systems

Flash memory is now a critical component in building embedded or portable devices because of its nonvolatile, shock-resistant, and power-economic nature. With the very different characteristics of flash memory, mechanisms proposed for many block-oriented storage media cannot be directly applied to flash memory. Distinct from the past work, we propose an adaptive striping architecture to significantly boost the system performance. The capability of the proposed mechanisms and architecture is demonstrated over realistic prototypes and workloads.

On efficient wear leveling for large-scale flash-memory storage systems

Flash memory won its edge over many other storage media for embedded systems, because it provides better tolerance to the extreme environments which embedded systems are exposed to. In this paper, techniques referred to as wear leveling for the lengthening of flash-memory overall lifespan are considered. This paper presents the dual-pool algorithm, which realizes two key ideas: To cease the wearing of blocks by storing cold data, and to smartly leave alone blocks until wear leveling takes effect. The proposed algorithm requires no complicated tuning, and it resists changes of spatial locality in workloads. Extensive evaluation and comparison were conducted, and the merits of the proposed algorithm are justified in terms of wear-leveling performance and resource conservation.

Real-time garbage collection for flash-memory storage systems of real-time embedded systems

Flash-memory technology is becoming critical in building embedded systems applications because of its shock-resistant, power economic, and nonvolatile nature. With the recent technology breakthroughs in both capacity and reliability, flash-memory storage systems are now very popular in many types of embedded systems. However, because flash memory is a write-once and bulk-erase medium, we need a translation layer and a garbage-collection mechanism to provide applications a transparent storage service. In the past work, various techniques were introduced to improve the garbage-collection mechanism. These techniques aimed at both performance and endurance issues, but they all failed in providing applications a guaranteed performance. In this paper, we propose a real-time garbage-collection mechanism, which provides a guaranteed performance, for hard real-time systems. On the other hand, the proposed mechanism supports non-real-time tasks so that the potential bandwidth of the storage system can be fully utilized. A wear-leveling method, which is executed as a non-real-time service, is presented to resolve the endurance problem of flash memory. The capability of the proposed mechanism is demonstrated by a series of experiments over our system prototype.

Hybrid solid-state disks: combining heterogeneous NAND flash in large SSDs

NAND-flash-based SSDs (solid-state disks) are recently used in embedded computers to replace power-hungry disks. This paper presents a hybrid approach to large SSDs, which combines MLC flash and SLC flash. The idea is to complement the drawbacks of the two kinds of NAND flash with each others advantages. The technical issues of the design of a hybrid SSD pertain to data placement and wear leveling over heterogeneous NAND flash. Our experimental results show that, by adding a 256 MB SLC flash to a 20 GB MLC-flash array, the hybrid SSD improves over a conventional SSD by 4.85 times in terms of average response. The average throughput and energy consumption are improved by 17 % and 14%, respectively. The hybrid SSD is only 2% more expensive than a purely MLC-flash-based SSD, for which the extra cost is limited and very rewarded.

Efficient identification of hot data for flash memory storage systems

Hot data identification for flash memory storage systems not only imposes great impacts on flash memory garbage collection but also strongly affects the performance of flash memory access and its lifetime (due to wear-levelling). This research proposes a highly efficient method for on-line hot data identification with limited space requirements. Different from past work, multiple independent hash functions are adopted to reduce the chance of false identification of hot data and to provide predictable and excellent performance for hot data identification. This research not only offers an efficient implementation for the proposed framework, but also presents an analytic study on the chance of false hot data identification. A series of experiments was conducted to verify the performance of the proposed method, and very encouraging results are presented.

An Efficient B-Tree Layer for Flash-Memory Storage Systems

With a significant growth of the markets for consumer electronics and various embedded systems, flash memory is now an economic solution for storage systems design. For index structures which require intensively fine-grained updates/modifications, block-oriented access over flash memory could introduce a significant number of redundant writes. It might not only severely degrade the overall performance but also damage the reliability of flash memory. In this paper, we propose a very different approach which could efficiently handle fine-grained updates/modifications caused by B-Tree index access over flash memory. The implementation is done directly over the flash translation layer (FTL) such that no modifications to existing application systems are needed. We demonstrate that the proposed methodology could significantly improve the system performance and, at the same time, reduce the overheads of flash-memory management and the energy dissipation, when index structures are adopted over flash memory.

Efficient management for large-scale flash-memory storage systems with resource conservation

Many existing approaches on flash-memory management are based on RAM-resident tables in which one single granularity size is used for both address translation and space management. As high-capacity flash memory is becoming more affordable than ever, the dilemma of how to manage the RAM space or how to improve the access performance is emerging for many vendors. In this article, we propose a tree-based management scheme which adopts multiple granularities in flash-memory management. Our objective is to not only reduce the run-time RAM footprint but also manage the write workload, due to housekeeping. The proposed method was evaluated under realistic workloads, where significant advantages over existing approaches were observed, in terms of the RAM space, access performance, and flash-memory lifetime.

An efficient management scheme for large-scale flash-memory storage systems

Flash memory is among the top choices for storage media in ubiquitous computing. With a strong demand of high-capacity storage devices, the usages of flash memory quickly grow beyond their original designs. The very distinct characteristics of flash memory introduce serious challenges to engineers in resolving the quick degradation of system performance and the huge demand of main-memory space for flash-memory management when high-capacity flash memory is considered. Although some brute-force solutions could be taken, such as the enlarging of management granularity for flash memory, we showed that little advantage is received when system performance is considered. This paper proposes a flexible management scheme for large-scale flash-memory storage systems. The objective is to efficiently manage high-capacity flash-memory storage systems based on the behaviors of realistic access patterns. The proposed scheme could significantly reduce the main-memory usages without noticeable performance degradation.

An efficient R-tree implementation over flash-memory storage systems

For many applications with spatial data management such as Geographic Information Systems (GIS), block-oriented access over flash memory could introduce a significant number of node updates. Such node updates could result in a large number of out-place updates and garbage collection over flash memory and damage its reliability. In this paper, we propose a very different approach which could efficiently handle fine-grained updates due to R-tree index access of spatial data over flash memory. The implementation is done directly over the flash translation layer (FTL) without any modifications to existing application systems. The feasibility of the proposed methodology is demonstrated with significant improvement on system performance, overheads on flash-memory management, and energy dissipation.

Efficient on-line identification of hot data for flash-memory management

Hot-data identification for flash-memory storage systems not only imposes great impacts on flash-memory garbage collection but also strongly affects the performance of flashmemory access and its life time (due to wear-levelling). In this research, we propose a highly efficient method for online hot-data identification with limited space requirements. Different from the past work, multiple independent hash functions are adopted to reduce the chance of false identification of hot data and provide predictable and excellent performance for hot-data identification. We not only propose an efficient implementation of the proposed framework but also conduct a series of experiments to verify the performance of the proposed method, in which very encouraging results are presented.