throbber
IEEE T R A N S A C T I O N S O N
`
`COMPUTERS
`
`January 2010
`
`VOLUME 59
`
`NUMBER 1
`
`A publication of the IEEE Computer Society
`Indexed in ISI
`(ISSN 0018-9340)
`
`ITCOB4
`
`RegulaR PaPeRs
`Distributed Systems
`A Novel Weighted-Graph-Based Grouping Algorithm for Metadata Prefetching
`P. Gu, J. Wang, Y. Zhu, H. Jiang, and P. Shang .................................................................................................................. 1
`Interconnection networks
`Heterogeneous Interconnects for Energy-Efficient Message Management in CMPs
`A. Flores, J.L. Aragón, and M.E. Acacio .............................................................................................................................
`Network-on-Chip Hardware Accelerators for Biological Sequence Alignment
`S. Sarkar, G.R. Kulkarni, P.P. Pande, and A. Kalyanaraman ..............................................................................................
`Load Balancing and Task assignment
`Communication-Aware Load Balancing for Parallel Applications on Clusters
`X. Qin, H. Jiang, A. Manzanares, X. Ruan, and S. Yin .......................................................................................................
`Memory and Storage Management
`Improving Flash Wear-Leveling by Proactively Moving Static Data
`Y.-H. Chang, J.-W. Hsieh, and T.-W. Kuo ............................................................................................................................
`Modeling and Performance Evaluation
`Model-Driven System Capacity Planning under Workload Burstiness
`G. Casale, N. Mi, and E. Smirni .........................................................................................................................................
`Security
`Conversion Algorithms and Implementations for Koblitz Curve Cryptography
`B.B. Brumley and K.U. Järvinen .........................................................................................................................................
`Theory and algorithms
`Independent Spanning Trees on Multidimensional Torus Networks
`S.-M. Tang, J.-S. Yang, Y.-L. Wang, and J.-M. Chang .........................................................................................................
`Scalable Node-Level Computation Kernels for Parallel Exact Inference
`Y. Xia and V.K. Prasanna ....................................................................................................................................................
`Verification
`Integrating Evolutionary Computation with Abstraction Refinement for Model Checking
`F. He, X. Song, W.N.N. Hung, M. Gu, and J. Sun ...............................................................................................................
`BRief ContRiButions
`Predictive Temperature-Aware DVFS
`J.S. Lee, K. Skadron, and S.W. Chung ...............................................................................................................................
`Proofs of Correctness and Properties of Integer Adder Circuits
`G. Chen and F. Liu .............................................................................................................................................................
`(Contents continued on back cover)
`
`16
`
`29
`
`42
`
`53
`
`66
`
`81
`
`93
`
`103
`
`116
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`127
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`134
`
`www.computer.org
`tc@computer.org
`
`IEEE TRANSACTIONS ON COMPUTERS
`
`Vol. 59, No. 1, January 2010
`
`(Contents continued from front cover)
`
`Comments
`Comment on “Performability Analysis: A New Algorithm”
`V. Suñé, J.A. Carrasco, H. Nabli, and B. Sericola ..............................................................................................................
`2009 Annual Reviewers List ...............................................................................................................................................
`
`137
`139
`
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`revised 1 May 2009
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`Micron Ex. 1065, p. 1
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`Micron Ex. 1065, p. 2
`Micron v. Vervain
`IPR2021-01547
`
`

`

`IEEE TRANSACTIONS ON COMPUTERS, VOL. 59, NO. 1,
`
`JANUARY 2010
`
`53
`
`Improving Flash Wear-Leveling
`by Proactively Moving Static Data
`
`Yuan-Hao Chang, Member, IEEE, Jen-Wei Hsieh, Member, IEEE, and
`Tei-Wei Kuo, Senior Member, IEEE
`
`Abstract—Motivated by the strong demand for flash memory with enhanced reliability, this work attempts to achieve improved flash-
`memory endurance without substantially increasing overhead and without excessively modifying popular implementation designs such
`as the Flash Translation Layer protocol (FTL), NAND Flash Translation Layer protocol (NFTL), and Block-Level flash translation layer
`protocol (BL). A wear-leveling mechanism for moving data that are not updated is proposed to distribute wear-leveling actions over the
`entire physical address space, so that static or rarely updated data can be proactively moved and memory-space requirements can be
`minimized. The properties of the mechanism are then explored with various implementation considerations. A series of experiments
`based on a realistic trace demonstrates the significantly improved endurance of FTL, NFTL, and BL with limited system overhead.
`
`Index Terms—Flash memory, wear leveling, endurance, reliability.
`

`
`1 INTRODUCTION
`
`WHILE flash memory remains one of the most popular
`
`storage-system designs for embedded systems, its
`applications have far surpassed its original design goals. The
`two popular NAND flash-memory designs are Single Level
`Cell (SLC) flash memory and Multiple Level Cell (MLC)
`flash memory. Each SLC flash-memory cell can accommo-
`date one-bit information while each MLCn flash-memory
`cell can contain n-bit information. As n increases, the
`endurance of each block in MLC flash memory decreases
`substantially. In recent years, flash memory has become one
`part or layer in many system designs. Well-known examples
`are the flash-memory cache of hard disks proposed by Intel
`[2], [3], [4] and the Microsoft Windows Vista fast booting
`service [5], [6]. Such developments reveal the limitations of
`flash memory, especially in terms of endurance.
`The reliability of
`flash memory is an even more
`challenging problem now that
`low-cost
`flash-memory
`designs are gaining market momentum [7]. For example,
`the endurance of an MLC2 flash-memory block is only
`10,000 (or 5,000) erase cycles whereas that of its SLC flash-
`memory counterpart is 100,000 erase cycles [8], [9]. As the
`number of bits of
`information per cell would keep
`increasing for MLC in the near future, the endurance of a
`block might also get worse, such as few thousand or even
`hundred erase cycles. This underlines the reliability issue of
`
`. Y.-H. Chang and T.-W. Kuo are with the Department of Computer Science
`and Information Engineering, Graduate Institute of Networking and
`Multimedia, National Taiwan University, CSIE Building, No. 1, Sec. 4,
`Roosevelt Rd., Taipei 106, Taiwan.
`E-mail: {johnson, ktw}@csie.ntu.edu.tw.
`. J.-W. Hsieh is with the Department of Computer Science and
`Information Engineering, National Taiwan University of Science and
`Technology, IB-1032, CSIE, No. 43, Sec. 4, Keelung Rd., Taipei 106,
`Taiwan. E-mail: jenwei@mail.ntust.edu.tw.
`
`Manuscript received 20 Sept. 2007; revised 1 Mar. 2009; accepted 11 Mar.
`2009; published online 10 Sept. 2009.
`Recommended for acceptance by A. Gonzalez.
`For information on obtaining reprints of this article, please send e-mail to:
`tc@computer.org, and reference IEEECS Log Number TC-2007-09-0476.
`Digital Object Identifier no. 10.1109/TC.2009.134.
`
`flash memory. However, improving reliability is proble-
`matic because flash-memory designs allow little compro-
`mise between system performance and cost, especially for
`low-cost devices. These observations motivate the current
`proposal
`for enhancing flash-memory endurance with
`minor modifications of popular implementation designs.
`A NAND flash-memory chip contains many blocks, and
`each block consists of a fixed number of pages. A block is
`the smallest unit involved in erase operations while read
`and write operations are done in pages. Each page of
`small-block(/large-block) SLC flash memory can store
`512 B(/2 KB) data, and each block contains 32(/64) pages.
`The configuration of MLC2 flash memory is the same as
`that of large-block SLC flash memory, except that each
`block is composed of 128 pages [10]. Notably, recent MLC
`designs are intended to reduce costs by employing large-
`capacity pages such as 4 KB pages and 8 KB pages [11].
`Flash memory is usually managed by a block-device-
`emulating layer so that file systems can be built on top (see
`below discussion regarding Flash Translation Layer proto-
`col (FTL), NAND Flash Translation Layer protocol (NFTL),
`and Block-Level flash translation layer protocol (BL)). Such
`a layer implementation can be carried out by either software
`on a host system (as a raw medium) or hardware/firmware
`on the flash device. In the past decade, numerous research
`results and implementation designs have been proposed to
`satisfy the performance needs of flash-memory storage
`systems, e.g., [12], [13], [14], [15], [16], [17], [18], [19], [20].
`Some authors have proposed different system architectures
`and layer designs, e.g., [16], [17], [18], and some have
`exploited large-scaled storage systems and data compres-
`sion, e.g., [19], [20].
`Given the characteristics of flash memory in out-place
`updates, data to be updated must be written to another
`page of flash memory, where the original page of the data is
`marked as invalid. The out-place-update characteristic
`results in the wear-leveling issue over flash memory
`because any recycling of invalid pages introduces block
`
`0018-9340/10/$26.00 ß 2010 IEEE
`Published by the IEEE Computer Society
`Authorized licensed use limited to: Kaytlyn Snyder. Downloaded on September 21,2022 at 21:57:08 UTC from IEEE Xplore. Restrictions apply.
`
`Micron Ex. 1065, p. 3
`Micron v. Vervain
`IPR2021-01547
`
`

`

`54
`
`IEEE TRANSACTIONS ON COMPUTERS, VOL. 59, NO. 1,
`
`JANUARY 2010
`
`erasing. The objective of wear leveling is to evenly
`distribute block erases over flash memory so that flash-
`memory endurance can be improved. Thus, various
`promising approaches based on dynamic wear leveling
`have been proposed, e.g., [16], [21], [22], [23], [24]. Dynamic
`wear leveling achieves wear leveling by recycling blocks of
`hot (i.e., dynamic or frequently updated) data areas. Such
`approaches require efficient identification of hot data, and
`several excellent designs have been proposed, e.g., [16],
`[25],
`[26],
`[27]. Although dynamic wear leveling does
`substantially enhance wear leveling, the endurance im-
`provement is stringently constrained by its nature: that is,
`blocks of cold data (i.e., infrequently updated data) are
`likely to stay intact regardless of whether updates of hot
`data wear out other blocks. In other words, updates and
`recycling of blocks/pages will only happen to blocks that
`are free or occupied by hot data.
`to dynamic wear
`Static wear leveling is orthogonal
`leveling.
`Its objective is to proactively move static or
`infrequently updated data to other locations so as to
`prevent any static or infrequently updated data from
`staying at any block for a long period of time. Thus, wear
`leveling can be evenly applied to all blocks. For example,
`assume that an extremely large quantity of data must be
`written or even repeatedly updated to a 2 GB flash memory.
`If cold data, such as movies, comprise 1.5 GB of the flash
`memory, then updating those data would be constrained in
`the reusing of blocks in the one fourth of the flash memory.
`No dynamic wear leveling can effectively resolve the
`endurance problem because cold data always stay at their
`blocks. Only static wear leveling can ensure that blocks are
`evenly utilized and have even erase counts because it can
`keep cold data from staying at their blocks.
`Although static wear leveling was first defined in early
`2000, e.g., [28], there are still limited results reported in the
`literature [21], [22], [28] due to historic market needs.
`Particularly, Ban and Hasbaron proposed randomly erasing
`blocks after a fixed number of erase or write requests [22].
`Ban and Hasbaron’s algorithm was analyzed both theore-
`tically and experimentally by Ben-Aroya and Toledo and
`later implemented by Spivak and Toledo in a flash-based
`storage system [29], [30]. Although Ban and Hasbaron’s
`algorithm has proven being effective, the algorithm has
`difficulty in identifying hot and cold data. Its inadequate
`tracking of data-access locality limits its effectiveness since
`hot data are frequently updated and are not necessary to be
`moved around by a wear-leveling algorithm. The popularity
`of MLC flash memory and the widespread adoption of flash
`memory in various products with high update frequencies
`have focused much attention on static wear leveling in
`recent years. For example, some recent
`flash-memory
`products have adopted Ban and Hasbaron’s algorithm for
`static wear leveling. The main technical challenge of static
`wear leveling is on the endurance improvement with
`respect to extra overheads that are considered reasonable
`in selected products.
`This study proposes a static wear-leveling mechanism for
`improving flash-memory endurance with limited memory-
`space requirements. Specifically, an adjustable housekeep-
`ing data structure is presented, and a cyclic-queue-based
`
`Fig. 1. The typical system architecture.
`
`scanning procedure is proposed for static wear leveling. The
`objective is to improve the endurance of flash memory with
`limited overhead and without excessively modifying
`popular implementation designs, such as FTL, NFTL, and
`BL. The behavior of the proposed mechanism was analyzed
`with respect to FTL, NFTL, and BL, on extra block erases,
`extra live-data copying, address translation time, space
`utilization, and main-memory requirements. A series of
`experiments was then conducted based on a realistic trace.
`The experiments revealed endurance improvements of
`103.37, 136.32, and 60.74 percent
`in FTL, NFTL, and
`BL-based storage systems, respectively, in terms of the first
`failure time (i.e., the first time that a worn-out block occurs).
`Erase-count distribution over blocks was also substantially
`improved.
`The rest of this paper is organized as follows: Section 2
`presents the system architectures and summarizes popular
`existing implementations of Flash Translation Layer for
`address translation and bloc

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