Kwanhu Bang

Run-Time Adaptive Workload Estimation for Dynamic Voltage Scaling

Dynamic voltage scaling (DVS) is a popular energy-saving technique for real-time tasks. The effectiveness of DVS critically depends on the accuracy of workload estimation, since DVS exploits the slack or the difference between the deadline and execution time. Many existing DVS techniques are profile based and simply utilize the worst-case or average execution time without estimation. Several recent approaches recognize the importance of workload estimation and adopt statistical estimation techniques. However, these approaches still require extensive profiling to extract reliable workload statistics and furthermore cannot effectively handle time-varying workloads. Feedback-control-based adaptive algorithms have been proposed to handle such nonstationary workloads, but their results are often too sensitive to parameter selection. To overcome these limitations of existing approaches, we propose a novel workload estimation technique for DVS. This technique is based on the Kalman filter and can estimate the processing time of workloads in a robust and accurate manner by adaptively calibrating estimation error by feedback. We tested the proposed method with workloads of various characteristics extracted from eight MPEG video clips. To thoroughly evaluate the performance of our approach, we used both a cycle-accurate simulator and an XScale-based test board. Our simulation result demonstrates that the proposed technique outperforms the compared alternatives with respect to the ability to meet given timing and Quality of Service constraints. Furthermore, we found that the accuracy of our approach is almost comparable to the oracle accuracy achievable only by offline analysis. Experimental results indicate that using our approach can reduce energy consumption by 57.5% on average, only with negligible deadline miss ratio (DMR) around 6.1%. Moreover, the average of computational overheads for the proposed technique is just 0.3%, which is the minimum value compared to other methods. More importantly, the DMR of our method is bounded by 11.7% in the worst case, while those of other methods are twice or more than ours.

Design and analysis of flash translation layers for multi-channel NAND flash-based storage devices

NAND flash-based storage devices (NFSDs) have been replacing the conventional magnetic storage devices in many consumer electronic systems. One of the advantages of NFSDs is their read/write bandwidth, which is higher than that of the magnetic storage devices. For further increase of their bandwidth, high-end NFSDs employ multichannel and multi-way architectures in which it is possible to access the NAND flash memories (NFMs) in parallel for amortizing the long latency of NFMs. Even though this architecture provides higher bandwidth from the hardware perspective, the overall performance of an NFSD critically depends on how efficiently the multiple channels and ways are utilized. In this regard, the key design component is an intermediate software layer called flash translation layer (FTL), since it manages the hardware resources as well as data. To the best of authorsiquest knowledge, this is the first work to propose a general method to design an FTL for multichannel / multi-way NFSDs (FTL-MM). The proposed design method consists of two steps. First, we design an FTL for a single-channel / single-way NFSD (FTL-SS). Second, we extend the FTL to support an NFSD with an arbitrary number of channels and ways. To prove the generality and effectiveness of the proposed method, we apply the method to three well-known FTLs. The experimental results indicate that the FTLs enhanced by our approach are comparable to the ideal FTL and that their performance is scalable to various channel / way architectures. Quantitatively speaking, the average channel utilization decreases by at most 10%, when we increase the number of channels and ways up to four.

Architecture Exploration of High-Performance PCs with a Solid-State Disk

As the cost per bit of NAND flash memory devices rapidly decreases, NAND-flash-based Solid-State Disks (SSDs) are replacing Hard Disk Drives (HDDs) used in a wide spectrum of consumer computing devices. Although typical SSDs can deliver higher performances than HDDs can, the full capabilities of SSDs are currently not exploited in most systems. This is because an SSD is interfaced with its host system using the architectures and interface protocols designed for HDDs, due to compatibility issues. Given the pace at which the stand-alone performance of SSDs improves, the performance loss of SSDs due to the legacy interface and system architecture will soon become intolerable. To address this issue, we propose several architectural choices to fully exploit the performance of SSDs used in consumer PC architectures. More specifically, we explore its interface scheme, and data transfer concurrency with the change of the conventional PC architecture if necessary. We evaluated the performance of the architectural choices by prototyping them with SystemC. The experimental results guide us how to trade off the performance enhancement and the change of the PC architecture. The performance improvement was maximized by 2.67 times when the PC architecture is changed to support a dual-port SSD connected to the North Bridge via the Double-Data Rate (DDR) interface in real trace environments.

An Adaptive Idle-Time Exploiting Method for Low Latency NAND Flash-Based Storage Devices

The market share of NAND flash-based storage devices (NFSDs) has rapidly grown in recent years since many characteristics, such as non-volatility, low latency, and high reliability, meet the requirements for various types of storage devices. However, the unique characteristic of NAND flash memories (NFMs), erase-before-write, causes problems for NFSDs from a performance perspective. Specifically, performance degradation is incurred by extra operations that serve to hide the bad characteristics of NFMs. In order to resolve this problem, many attractive methods have been proposed. Various algorithms for flash translation layers (FTLs) are representative methods that provide space redundancy to NFSDs for better performance. However, the amount of space redundancy is limited by the capacity of NFMs and thus, space redundancy is still insufficient for improving the performance of NFSDs. Consequently, a new type of redundancy, termed temporal redundancy, has recently been introduced for NFSDs. More precisely, the idleness of NFSDs is exploited so as to precede extra operations for NFSDs while minimizing the overhead of extra operations. In this paper, we propose an adaptive time-out method based on the Hidden-Markov Model (HMM) to efficiently utilize idle periods. In addition, we also suggest a simple scheduling scheme for extra operations that can be customized for general FTLs. The experimental results demonstrate that the proposed method yields performance improvements in terms of average write latency and peak latency, 74% and 76% better than the existing method, respectively, and approaching within average 9% and 5% of the optimal case, respectively.

Application-aware Design Parameter Exploration of NAND Flash Memory

NAND flash memory (NFM) based storage devices, e.g. Solid State Drive (SSD), are rapidly replacing conventional storage devices, e.g. Hard Disk Drive (HDD). As NAND flash memory technology advances, its specification has evolved to support denser cells and larger pages and blocks. However, efforts to fully understand their impacts on design objectives such as performance, power, and cost for various applications are often neglected. Our research shows this recent trend can adversely affect the design objectives depending on the characteristics of applications. Past works mostly focused on improving the specific design objectives of NFM based systems via various architectural solution when the specification of NFM is given. Several other works attempted to model and characterize NFM but did not access the system-level impacts of individual parameters. To the best of our knowledge, this paper is the first work that considers the specification of NFM as the design parameters of NAND flash storage devices (NFSDs) and analyzes the characteristics of various synthesized and real traces and their interaction with design parameters. Our research show that optimizing design parameters depends heavily on the characteristics of applications. The main contribution of this research is to understand the effects of low-level specifications of NFM, e.g. cell type, page size, and block size, on system-level metrics such as performance, cost, and power consumption in various applications with different characteristics, e.g. request length, update ratios, read-and-modify rations. Experimental results show that the optimized page and block size can achieve up to 15 times better performance than the conventional NFM configuration in various applications. The results can be used to optimize the system-level objectives of a system with specific applications, e.g. embedded systems with NFM chips, or predict the future direction of NFM.

A memory hierarchy-aware metadata management technique for Solid State Disks

Solid State Disk (SSD) drives are rapidly replacing conventional hard disk drives (HDDs) due to their remarkable performance gains. For emulating HDDs, SSDs require a flash translation layer (FTL) which hides the out-of-place-update feature of NAND flash memories. In the latest large-capacity SSDs, FTLs must manage huge metadata such as a logical-to-physical address mapping table, a pool of free blocks, or a list of garbage blocks with their erase counts. The total metadata cannot reside on a small on-chip SRAM so that it must be hierarchically distributed in DRAM or NAND flash memories. This paper presents an efficient metadata management technique for SSDs which fully exploits memory hierarchy of an SSD. By the proposed technique, the distributed metadata can be efficiently searched or updated with small overheads. Experimental results show that overheads of metadata management become considerably large in the latest SSDs and they are minimized efficiently by the proposed technique.

Extended MPEG Video Format for Efficient Dynamic Voltage Scaling

We present an extended MPEG video format for efficient Dynamic Voltage Scaling (DVS). DVS technique has been widely researched, but the execution time variation of a periodic task (i.e. MPEG decoding) is still a challenge to be tackled. Unlike previous works, we focus on the data (video stream) rather than the execution code to overcome such limitation. The proposed video format provides the decoding costs of frames to help the precise prediction of their execution times at client machines. The experimental results show that the extended format only increases the data size less than 1% by adding about 10 bits representing the decoding cost of each frame. Also, a DVS technique adjusted for the proposed format achieves 90% of efficiency compared to the oracle case, while keeping the run time overhead of the technique negligible.