Space-Efficient Predictive Block Management

With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes

Cross-Layer Optimization Techniques for Improving Performance and Reliability of NAND Flash-Based Storage Systems

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김지홍.As the cost-per-bit of NAND flash memory is quickly improved by advanced process technologies and multi-leveling techniques, NAND flash-based storage systems are widely employed from mobile embedded systems to high-end enterprise server systems. Although the advanced process and device techniques have greatly improved the cost-per-bit of NAND flash memory, they have also significantly degraded the performance and reliability of NAND flash memory as key side effects of the advanced techniques. In order for NAND flash-based storage systems to be more broadly used in various computing environments, it is critical to overcome the performance and reliability problems of recent high-density NAND flash memory in a satisfactory fashion. In this dissertation, we argue that cross-layer optimization techniques, which vertically integrate various optimization factors from different design abstraction levels, can play key roles in improving performance and reliability of high-density NAND flash memory. First, we propose read-disturb management techniques which reduce the expensive read-disturb management overheads while maintaining reliability of NAND flash memory. An FTL using the read-disturb management module, called redFTL, alleviates highly skewed read accesses to a small part of NAND flash memory into more balanced read accesses to a large number of blocks, thus reducing data migrations needed for avoiding read-disturb errors. As an extended version of redFTL, we propose an integrated read-disturb management technique, called redFTL+, which fundamentally solves read-disturb problems by exploiting a tradeoff between the read disturbance and write speed. By modifying NAND chips to support multiple read modes with different read voltages and write speeds, redFTL+ intelligently allocates frequently-read data to read-resistant blocks. Since the read disturbance is also proportional to the read time, redFTL+ takes advantage of the difference in the read time among different NAND pages by reallocating read-intensive data to read-resistant pages. Second, we propose data separation techniques which reduce garbage collection overhead. We propose a program context-aware data separation technique, called PDS, which can reduce the garbage collection overhead by exploiting program context hints. By using a program context, which serves as a proper granularity of maintaining data update behavior, PDS helps an FTL gather data with similar update times to the same blocks. As an improved version of PDS, we propose an integrated data separation technique, called IDS, which uses both update history of NAND device and program context hints for predicting data update behaviors. By classifying data based on the cross-layer information, an FTL using IDS can make more dead or near-dead blocks over PDS, thus reducing the garbage collection overhead. In order to evaluate the effectiveness of the proposed techniques, we performed a series of evaluations using both a simulator and an emulator with I/O traces which were collected from various systems. Our experimental results show that cross-layer optimization techniques are more effective over our single-layer optimization techniques. RedFTL+ decreases the read-disturb management overhead on average by 24% over redFTL. The IDS-based FTL decreases the garbage collection overhead on aver-age by 18% over the PDS-based FTL. The evaluation results demonstrate that our cross-layer optimization techniques improve an overall performance of NAND-based storage systems over previous single-layered optimization techniques by reducing overheads from read-disturb management and garbage collection while maintaining the reliability of the storage systems.Contents I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Read-Disturb Problem . . . . . . . . . . . . . . . . 2 1.1.2 Garbage Collection Problem . . . . . . . . . . . . . 4 1.2 Research Goals and Contributions . . . . . . . . . . . . . . 7 1.3 Dissertation Structure . . . . . . . . . . . . . . . . . . . . . 9 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1 NAND Flash Memory . . . . . . . . . . . . . . . . . . . . 11 2.2 System Software for NAND Flash Memory . . . . . . . . . 17 2.3 NAND Flash-Based Storage Devices . . . . . . . . . . . . . 18 2.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.1 Read-Disturb Techniques . . . . . . . . . . . . . . . 20 2.4.2 Data Separation Techniques . . . . . . . . . . . . . 21 III. A Single-Layered Read Disturb Management Technique . . . 24 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2 Performance Implications of Read Disturbs . . . . . . . . . 28 3.2.1 Effect of Frequent Read Reclaims . . . . . . . . . . 28 3.2.2 Effect of Read Reclaims on Response Time Fluctu- ations . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.3 Effect of SSD Read Buffer on Read Reclaims . . . . 31 3.3 Read Disturb Management Techniques . . . . . . . . . . . . 32 3.3.1 Data Distribution Technique . . . . . . . . . . . . . 32 3.3.2 Proactive Data Migration . . . . . . . . . . . . . . . 35 3.4 RedFTL: Read Disturb-Aware FTL . . . . . . . . . . . . . . 35 3.4.1 Overview of RedFTL . . . . . . . . . . . . . . . . . 35 3.4.2 Read-Hot Page Separation . . . . . . . . . . . . . . 37 3.4.3 Good Block Pool Management . . . . . . . . . . . . 38 3.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . 38 IV. An Integrated Approach for Read Disturb Management . . . 43 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Read Disturb Management Techniques . . . . . . . . . . . . 46 4.2.1 Mitigation of Read Reclaims by Read Voltage Scaling 47 4.2.2 Mitigation of Read Reclaims by Read Operation Time Scaling . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2.3 NAND Read-Disturbance Model . . . . . . . . . . . 55 4.3 Design and Implementation of RedFTL+ . . . . . . . . . . . 57 4.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.2 Dynamic Mode Selection . . . . . . . . . . . . . . . 58 4.3.3 Distributed Migration to RRBs . . . . . . . . . . . . 59 4.3.4 Read-Hotness Detection . . . . . . . . . . . . . . . 61 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . 63 V. A Single-Layered Data Separation Technique . . . . . . . . . 70 5.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.1.1 Frequency-Based Data Separation . . . . . . . . . . 70 5.1.2 Garbage Collection Using ORA . . . . . . . . . . . 73 5.1.3 Evaluation of Existing Locality-based Heuristic . . . 74 5.2 Correlation between Program Contexts and Updates . . . . 78 5.3 PDS: Program Context-Aware Data Separation Technique . . 82 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . 87 VI. An Integrated Data Separation Technique . . . . . . . . . . . 93 6.1 Limitations of Single-Layered Program Context-Aware Data Separation Technique . . . . . . . . . . . . . . . . . . . . . 93 6.2 IDS: Integrated Data Separation Technique . . . . . . . . . 94 6.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . 94 6.2.2 Determination of Update Program Context . . . . . 96 6.2.3 Dynamic Clustering Program Contexts Based On Update Locality . . . . . . . . . . . . . . . . . . . . 96 6.2.4 Managing The Hot Data Associated with An Update Program Context . . . . . . . . . . . . . . . . 103 6.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . 104 VII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 114 7.2.1 Improving QoS of RedFTL+ by Exploiting Program Context Hints . . . . . . . . . . . . . . . . . . 114 7.2.2 Mitigating Read-Disturb Problem by Read Disturb- Aware Read Buffer Management Technique . . . . . 115 7.2.3 Improving Efficiency of Garbage Collection by Adjusting GC Trigger Points . . . . . . . . . . . . . . 115 7.2.4 Improving Performance and Reliability of NAND Flash Memory by Integrating Various Techniques . . 117 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126Docto

EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE

The performance gap between processors and storage systems has been increasingly critical overthe years. Yet the performance disparity remains, and further, storage energy consumption israpidly becoming a new critical problem. While smarter caching and predictive techniques domuch to alleviate this disparity, the problem persists, and data storage remains a growing contributorto latency and energy consumption.Attempts have been made at data layout maintenance, or intelligent physical placement ofdata, yet in practice, basic heuristics remain predominant. Problems that early studies soughtto solve via layout strategies were proven to be NP-Hard, and data layout maintenance todayremains more art than science. With unknown potential and a domain inherently full of uncertainty,layout maintenance persists as an area largely untapped by modern systems. But uncertainty inworkloads does not imply randomness; access patterns have exhibited repeatable, stable behavior.Predictive information can be gathered, analyzed, and exploited to improve data layouts. Ourgoal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts byreplicating data at the storage device level.We present a comprehensive discussion of the design and construction of such a predictive engine,including workload evaluation, where we present and evaluate classical workloads as well asour own highly detailed traces collected over an extended period. We demonstrate significant gainsthrough an initial static grouping mechanism, and compare against an optimal grouping method ofour own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online)grouping, and provide motivation and solutions for addressing these challenges. These challengesinclude metadata storage, appropriate predictive collocation, online performance, and physicalplacement. We reduced the metadata needed by several orders of magnitude, reducing the requiredvolume from more than 14% of total storage down to less than 12%. We also demonstrate how ourcollocation strategies outperform competing techniques. Finally, we present our complete modeland evaluate a prototype implementation against real hardware. This model was demonstrated tobe capable of reducing device-level accesses by up to 65%

RAID Organizations for Improved Reliability and Performance: A Not Entirely Unbiased Tutorial (1st revision)

RAID proposal advocated replacing large disks with arrays of PC disks, but as the capacity of small disks increased 100-fold in 1990s the production of large disks was discontinued. Storage dependability is increased via replication or erasure coding. Cloud storage providers store multiple copies of data obviating for need for further redundancy. Varitaions of RAID based on local recovery codes, partial MDS reduce recovery cost. NAND flash Solid State Disks - SSDs have low latency and high bandwidth, are more reliable, consume less power and have a lower TCO than Hard Disk Drives, which are more viable for hyperscalers.Comment: Submitted to ACM Computing Surveys. arXiv admin note: substantial text overlap with arXiv:2306.0876