ACM Transactions on

Storage (TOS)

Latest Articles


Classic caching algorithms leverage recency, access count, and/or other properties of cached blocks at per-block granularity. However, for media such as flash which have performance and wear penalties for small overwrites, implementing cache policies at a larger granularity is beneficial. Recent research has focused on buffering small blocks and... (more)

Efficient and Available In-Memory KV-Store with Hybrid Erasure Coding and Replication

In-memory key/value store (KV-store) is a key building block for many systems like databases and large websites. Two key requirements for such systems... (more)


  • CFP - Special Issue on NVM and Storage (in detail)

  • TOS Editor-in-Chief featured in "People of ACM"
    Sam H. Noh is Editor-in-Chief of ACM Transactions on Storage (TOS) - featured in the periodic series "People of ACM", full article available here
    November 01, 2016

  • ACM Transaction on Storage (TOS) welcomes Sam H. Noh as its new Editor-in-Chief for a 3-year term, effective August 1, 2016.
    Sam H. Noh is a professor and Head of the School of the Electrical and Computer Engineering School at UNIST (Ulsan National Institute of Science and Technology) in Ulsan, Korea and a leader in the use of new memory technology such as flash memory and non-volatile memory in storage.
    - August 01, 2016

Forthcoming Articles

Systematic Erasure Codes with Optimal Repair Bandwidth and Storage

clfB-tree: Cacheline Friendly Persistent B-tree for NVRAM

Emerging byte-addressable non-volatile memory (NVRAM) is expected to replace block device storages as an alternative low latency persistent storage device. If NVRAM is used as a persistent storage device, a cache line instead of a disk page will be the unit of data transfer, consistency, and durability. In this work, we design and develop clfB-tree - a B-tree structure whose tree node fits in a single cache line. We employ existing write combining store buffer and restricted transactional memory (RTM) to provide a failure-atomic cache line write operation. Using the failure-atomic cache line write operations, we atomically update a clfB-tree node via a single cache line flush instruction without major changes in hardware. However, there exist many processors that do not provide SW interface for transactional memory. For those processors, our proposed clfB-tree achieves atomicity and consistency via in-place update, which requires maximum four cache line flushes. We evaluate the performance of clfB-tree on an NVRAM emulation board with ARM Cortex A-9 processor and a workstation that has Intel Xeon E7-4809 v3 processor. Our experimental results show clfB-tree outperforms wB-tree and CDDS B-tree by a large margin in terms of both insertion and search performance

Tiny-Tail Flash: Near-Perfect Elimination of Garbage Collection Tail Latencies in NAND SSDs

Flash storage has become the mainstream destination for storage users. However, SSDs do not always deliver the performance that users expect. The core culprit of flash performance instability is the well-known garbage collection (GC) process, which causes long delays as the SSD cannot serve (blocks) incoming I/Os, which then induces the long tail latency problem. We present ttFlash as a solution to this problem. ttFlash is a tiny-tail flash drive (SSD) that eliminates GC-induced tail latencies by circumventing GC-blocked I/Os with four novel strategies: plane-blocking GC, rotating GC, GC-tolerant read, and GC-tolerant flush. These four strategies leverage the timely combination of modern SSD internal technologies such as powerful controller, parity-based redundancies (RAIN), and capacitor-backed RAM. Our strategies are dependent on the use of intra-plane copyback operations. Through an extensive evaluation, we show that ttFlash comes significantly close to a no-GC scenario. Specifically, between 9999.99th percentiles, ttFlash is only 1.0 to 2.6x slower than the no-GC case, while a base approach suffers from 5138ms GC-induced slowdowns.

Application Crash Consistency and Performance with CCFS

Recent research has shown that applications often incorrectly implement crash consistency. We present the Crash-Consistent File System (ccfs), a file system that improves the correctness of application-level crash consistency protocols while maintaining high performance. A key idea in ccfs is the abstraction of a stream. Within a stream, updates are committed in program order, improving correctness; across streams, there are no ordering restrictions, enabling scheduling flexibility and high performance. We empirically demonstrate that applications running atop ccfs achieve high levels of crash consistency. Further, we show that ccfs performance under standard file-system benchmarks is excellent, in the worst case on par with the highest performing modes of Linux ext4, and in some cases notably better. Overall, we demonstrate that both application correctness and high performance can be realized in a modern file system.

vNFS: Maximizing NFS Performance with Compounds and Vectorized I/O

Modern systems use networks extensively, accessing both services and storage across local and remote networks. Latency is a key performance challenge, and packing multiple small operations into fewer large ones is an effective way to amortize that cost, especially after years of significant improvement in bandwidth but not latency. To this end, the NFSv4 protocol supports a compounding feature to combine multiple operations. Yet compounding has been underused since its conception because the synchronous POSIX file-system API issues only one (small) request at a time. We propose vNFS, an NFSv4.1-compliant client that exposes a vectorized high-level API and leverages NFS compound procedures to maximize performance. We designed and implemented vNFS as a user-space RPC library that supports an assortment of bulk operations on multiple files and directories. We found it easy to modify several UNIX utilities, an HTTP/2 server, and Filebench to use vNFS. We evaluated vNFS under a wide range of workloads and network latency conditions, showing that vNFS improves performance even for low-latency networks. On high-latency networks, vNFS can improve performance by as much as two orders of magnitude.

Modeling Drive-Managed SMR Performance

Accurately modeling drive-managed SMR disks is a challenge, requiring an array of approaches including both existing disk modeling techniques as well as new techniques for inferring internal translation layer algorithms. In this work we present the first predictive simulation model of a generally-available drive-managed SMR disk. Despite the use of unknown proprietary algorithms in this device, our model that is derived from external measurements is able to predict mean latency within a few percent, and with an RMS cumulative latency error of 25% or less for most workloads tested. These variations, although not small, are in most cases less than three times the drive-to-drive variation seen among seemingly identical drives.

HiNFS: A Persistent Memory File System with both Buffering and Direct-Access

Persistent memory provides data persistence at main memory with emerging non-volatile main memories (NVMMs). Recent persistent memory file systems aggressively use direct access, which directly copy data between user buffer and the storage layer, to avoid the double-copy overheads through the OS page cache. However, we observe they all suffer from slow writes due to NVMMs asymmetric read-write performance and much slower performance than DRAM. In this paper, we propose HiNFS, a high performance file system for non-volatile main memory, to combine both buffering and direct access for fine-grained file system operations. HiNFS uses an NVMM-aware Write Buffer to buffer the lazy-persistent file writes in DRAM, while performing direct access to NVMM for eager- persistent file writes. It directly reads file data from both DRAM and NVMM, by ensuring read consistency with a combination of the DRAM Block Index and Cacheline Bitmap to track the latest data between DRAM and NVMM. HiNFS also employs a Buffer Benefit Model to identify the eager-persistent file writes before issuing I/Os. Evaluations show that HiNFS significantly improves throughput by up to 184% and reduces execution time by up to 64% comparing with state-of-the-art persistent memory file systems PMFS and EXT4-DAX.

Hybris: Robust Hybrid Cloud Storage

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources. Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds. We implemented Hybris in Java and evaluated it using a series of micro and macro-benchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

SUPA: A Single Unified Read-Write Buffer and Pattern-Change-Aware FTL for the High Performance of Multi-Channel SSD

To design write buffer and FTL for SSD, previous studies have tried to increase overall performance by parallel I/O and garbage collection reduction. Recent works have proposed pattern-based management, which uses the request size and read- or write-intensiveness to apply different policies to each type of data. However, the locations of read and write requests are closely related, and the pattern of each type of data can be changed. In this work, we propose SUPA, a single unified read-write buffer and pattern-change-aware FTL on multi-channel SSD architecture. To increase both read and write hit ratios on the buffer based on locality, we use a single unified read-write buffer for both clean and dirty blocks. To handle pattern-changed blocks, we add a pattern handler between the buffer and the FTL. To reduce policy switching overhead for pattern-changed data, if pattern change is detected, pattern handler saves the corresponding data to the two locations handled by different policies respectively. In total, our evaluations show that SUPA can get up to 2.0 and 3.9 times less read and write latency, respectively, without loss of lifetime.

Building Efficient Key-Value Stores via a Light-weight Compaction Tree

Log-Structure Merge tree (LSM-tree) has been one of the mainstream indexes in key-value systems supporting a variety of write-intensive Internet applications in todays data centers. However, the performance of LSM-tree is seriously hampered by constantly occurring compaction procedures, which incur significant write amplification and degrade the write throughput. To alleviate the performance degradation caused by compactions, we introduce a light-weight compaction tree (LWC-tree), a variant of LSM-tree index optimized for minimizing the write amplification and maximizing the system throughput. The light-weight compaction drastically decreases write amplification by appending data in a table and only merging the metadata that has much smaller size. We have implemented three key-value LWC-stores based on the LWC-tree on different storage mediums. The LWC-store is particularly optimized for SMR drives as it eliminates the multiplicative I/O amplification from both LSM-trees and SMR drives. Due to the light-weight compaction procedure, LWC-store reduces the write amplification by a factor of up to 5× compared to the popular LevelDB key-value store. Moreover, the random write throughput of the LWC-tree on SMR drives is significantly improved by 467% even compared with LevelDB on conventional HDDs. Furthermore, LWC-tree has wide applicability and delivers impressive performance improvement in various conditions.

Ouroboros Wear-Leveling: A Two-Level Hierarchical Wear-Leveling Model for NVRAM

Emerging non-volatile RAM (NVRAM) have a limit on the number of writes that can be made to any cell. This motivates the need for wear-leveling to distribute the writes evenly among the cells. Unlike NAND Flash, cells in NVRAM can be rewritten without erasing the entire containing block, avoiding the issues of garbage collection, motivating alternate approaches to the problem. In this paper, we propose a hierarchical wear-leveling model called Ouroboros Wear-leveling. Ouroboros uses a two-level strategy whereby frequent low-cost intra-region wear-leveling at small granularity is combined with inter-region wear-leveling at a larger time interval and granularity. Ouroboros is a hybrid migration scheme that exploits correct demand predictions in making better wear-leveling decisions, while using randomization to avoid attacks by deterministic access patterns. We also propose a way to optimize wear-leveling parameters to meet a target smoothness level under limited time and space overhead constraints for different memory architectures and trace characteristics. Several experiments are performed on synthetically-generated memory traces with special characteristics, block-level storage traces, and memory-line-level memory traces. The results show that Ouroboros Wear-leveling can distribute writes smoothly across the whole NVRAM with up to 0.2% space overhead and 0.52% time overhead for a 512GB memory.

Redundancy Does Not Imply Fault Tolerance: Analysis of Distributed Storage Reactions to File-System Faults

We analyze how modern distributed storage systems behave in the presence of file-system faults such as data corruption and read and write errors. We characterize eight popular distributed storage systems and uncover numerous problems related to file-system fault tolerance. We find that modern distributed systems do not consistently use redundancy to recover from file-system faults: a single file-system fault can cause catastrophic outcomes such as data loss, corruption, and unavailability. We also find that the above outcomes arise due to fundamental problems in file-system fault handling that are common across many systems. Our results have implications for the design of next generation fault-tolerant distributed and cloud storage systems.

Introduction to the Special Issue on USENIX FAST 2017

Efficient Free Space Reclamation in WAFL

NetApp® WAFL® is a transactional file system that uses the copy-on-write mechanism to support fast write performance and efficient snapshot creation. However, copy-on-write increases the demand on the file system to find free blocks quickly, which makes rapid free space reclamation essential. Inability to find free blocks quickly may impede allocations for incoming writes. Efficiency is also important, because the task of reclaiming free space may consume CPU and other resources at the expense of client operations. In this paper, we describe the evolution (over more than a decade) of the WAFL algorithms and data structures for reclaiming space with minimal impact to the overall performance of the storage appliance.


Publication Years 2005-2017
Publication Count 223
Citation Count 1564
Available for Download 223
Downloads (6 weeks) 1308
Downloads (12 Months) 13910
Downloads (cumulative) 146843
Average downloads per article 658
Average citations per article 7
First Name Last Name Award
Sarita Adve ACM Fellows (2010)
Emery David Berger ACM Senior Member (2010)
Surendar Chandra ACM Senior Member (2009)
Alok Choudhary ACM Fellows (2009)
Deborah Estrin ACM Athena Lecturer Award (2006)
ACM Fellows (2000)
Jason Flinn ACM Fellows (2016)
Armando Fox ACM Karl V. Karlstrom Outstanding Educator Award (2015)
ACM Distinguished Member (2011)
ACM Senior Member (2009)
Gregory Ganger ACM Distinguished Member (2007)
Garth A Gibson ACM Fellows (2012)
ACM Doctoral Dissertation Award
Series Winner (1991)
Ramesh Govindan ACM Fellows (2011)
Ragib Hasan ACM Senior Member (2015)
John Heidemann ACM Senior Member (2007)
Tei-Wei Kuo ACM Fellows (2015)
Kai Li ACM Fellows (1998)
Ming Li ACM Fellows (2006)
Dahlia Malkhi ACM Fellows (2011)
Ethan L Miller ACM Distinguished Member (2013)
Walid Najjar ACM Distinguished Member (2015)
ACM Senior Member (2014)
Michael Reiter ACM Fellows (2008)
Stefan Savage ACM Prize in Computing (2015)
ACM Fellows (2010)
Steven Scott ACM Fellows (2012)
Kenneth C Sevcik ACM Fellows (1997)
Anand Sivasubramaniam ACM Distinguished Member (2010)
ACM Senior Member (2009)
Chandramohan A Thekkath ACM Fellows (2009)
Gene Tsudik ACM Fellows (2014)
ACM Senior Member (2013)
Amin Vahdat ACM Fellows (2011)
David Wagner ACM Doctoral Dissertation Award
Honorable Mention (2001) ACM Doctoral Dissertation Award
Honorable Mention (2001)
Randy Wang ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics (2007)
Marianne Winslett ACM Fellows (2006)
Tao Xie ACM Distinguished Member (2015)
ACM Senior Member (2011)
Philip S Yu ACM Fellows (1997)
Demetris Zeinalipour ACM Senior Member (2016)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Andrea Arpaci-Dusseau 10
Youjip Won 6
Teiwei Kuo 6
Remzi Arpaci-Dusseau 6
Erez Zadok 6
Ethan Miller 6
Dan Feng 6
Bianca Schroeder 5
Hyokyung Bahn 5
Cheng Chen 4
Raju Rangaswami 4
Remzi Arpaci-Dusseau 4
Changsheng XIE 4
Hong Jiang 4
Jiwu Shu 4
Qingsong Wei 4
Charles Wright 4
Randal Burns 4
Ilias Iliadis 4
Heonyoung Yeom 4
Narasimha Reddy 4
Weimin Zheng 4
Xiao Qin 4
Lidong Zhou 3
Nitin Agrawal 3
Eunji Lee 3
Youngjin Yu 3
Darrell Long 3
Suzhen Wu 3
Stergios Anastasiadis 3
Yuanhao Chang 3
Jenwei Hsieh 3
Song Jiang 3
Feng Chen 3
James Cipar 3
Vijayan Prabhakaran 3
Guangyan Zhang 3
An Wang 3
Lipin Chang 3
Samhyuk Noh 3
Yuanyuan Zhou 3
Xubin He 3
Jianxi Chen 3
Geoff Kuenning 3
Ohad Rodeh 3
Bo Mao 3
Hyeonsang Eom 3
Patrick Lee 3
John MacCormick 2
Kyuho Park 2
Jun Yang 2
Binbing Hou 2
André Brinkmann 2
Swaminathan Sundararaman 2
Sriram Subramanian 2
Leif Walsh 2
Prashant Pandey 2
Vinodh Venkatesan 2
Dongin Shin 2
Martín Farach-Colton 2
Lei Tian 2
Gregory Ganger 2
Jun Yuan 2
Roger Zimmermann 2
Tao Xie 2
Erik Riedel 2
Mahesh Balakrishnan 2
Ahmed Amer 2
Michael Bender 2
Taeho Hwang 2
Jaemin Jung 2
Jeffrey Chase 2
Eno Thereska 2
Scott Brandt 2
Wei Xue 2
Kiran Muniswamy-Reddy 2
Bradley Kuszmaul 2
Evangelos Eleftheriou 2
Xiaoyu Hu 2
Nikolai Joukov 2
Chinhsien Wu 2
Yang Zhan 2
Mark Storer 2
Kaladhar Voruganti 2
Lanyue Lu 2
Ashvin Goel 2
Shu Yin 2
Michael Swift 2
Gopalan Sivathanu 2
Alma Riska 2
Mahmut Kandemir 2
Arkady Kanevsky 2
Fred Douglis 2
Rob Johnson 2
Donald Porter 2
Zhan Shi 2
Daniel Fryer 2
Angela Brown 2
Jayanta Basak 2
Mario Blaum 2
Adam Manzanares 2
William Bolosky 2
Gene Tsudik 2
Bo Hong 2
Anand Sivasubramaniam 2
Xiaoning Ding 2
Yuehai Xu 2
Darrell Long 2
Chris Dragga 2
Sudhanva Gurumurthi 2
Mingdi Xue 2
Ali Tosun 2
Xiaojun Ruan 2
Jongmin Gim 2
Peter Reiher 2
Eitan Bachmat 2
Peter Desnoyers 2
William Jannen 2
Amogh Akshintala 2
Zhenmin Li 2
Leo Arulraj 1
Einar Mykletun 1
Weikeng Liao 1
Josef Bacik 1
Lars Nagel 1
Radu Sion 1
Deepak Bobbarjung 1
Walid Najjar 1
Abutalib Aghayev 1
Ao Ma 1
Mark Chamness 1
Seokhei Cho 1
Sooyong Kang 1
Pieter Hartel 1
John Heidemann 1
Xiaodong Li 1
Lingfang Zeng 1
Chandramohan Thekkath 1
Kirsten Hildrum 1
Philip YU 1
Seonho Kim 1
Ron Arnan 1
Venugopalan Ramasubramanian 1
Jin Li 1
Anton Kos 1
Veljko Milutinović 1
Kanchi Gopinath 1
Tudor Marian 1
Zhen Huang 1
Xiao Qin 1
Nguyen Tran 1
Frank Chiang 1
Yulai Xie 1
David Wagner 1
Lakshmi Bairavasundaram 1
Phillipa Gill 1
Ricardo Koller 1
Haifeng Yu 1
Dilma Silva 1
Kimberly Keeton 1
Kaushik Veeraraghavan 1
Windsor Hsu 1
Robert Hall 1
Adilet Kachkeev 1
Samuel Braunfeld 1
Alptekin Küpçü 1
Öznur Özkasap 1
Hyojun Kim 1
James Plank 1
Tianyu Wo 1
Yuchong Hu 1
Tao Xie 1
Fei Wu 1
Ping Huang 1
Jingui Wang 1
Junjie Ren 1
Kaiwei Li 1
Xiaosong Ma 1
Stephen Scott 1
Hyungkyu Chang 1
Asim Kadav 1
Sukwoo Kang 1
Sheng Qiu 1
Christina Strong 1
Soyoon Lee 1
Sanjeev Trika 1
Debra Hensgen 1
Hyeongseog Kim 1
Robert Haas 1
Kristal Pollack 1
Yankit Li 1
Kanchan Chandnani 1
Nan Su 1
Linjun Mei 1
Yongsoo Joo 1
Jehoshua Bruck 1
Feng Zheng 1
Liping Xiang 1
Matthew Wachs 1
Karan Sanghi 1
Fernando André 1
Paulo Sousa 1
Antony Rowstron 1
Daniel Ellard 1
Gordon Hughes 1
Charles Weddle 1
Mark Corner 1
John Douceur 1
Nitin Agrawal 1
Mingqiang Li 1
José Pereira 1
Mark Stanovich 1
Sangeetha Seshadri 1
Xiaolan Chen 1
Hyuck Han 1
Marshall McKusick 1
Peng Xu 1
Junbin Kang 1
Ye Zhai 1
Qing Liu 1
Jens Jelitto 1
Sunjin Lee 1
Yuxiang Ma 1
Krishna Kant 1
Tsansheng Hsu 1
Weikuan Shih 1
Pochun Huang 1
Picheng Hsiu 1
Rohit Jain 1
Joel Wolf 1
Phung Huynh 1
Aichun Pang 1
Junfeng Yang 1
Hyunjin Choi 1
Jaewoo Choi 1
Ningfang Mi 1
Vagelis Hristidis 1
Cheng Huang 1
Gaewon You 1
Jasna Milovanovic 1
Jaka Sodnik 1
Sara Stancin 1
Dan Feng 1
Chuan Qin 1
Jiyong Shin 1
Kevin Harms 1
William Allcock 1
Yubiao Pan 1
Ernst Biersack 1
Jianzhong Huang 1
Jinyang Li 1
Jiwu Shu 1
Amin Vahdat 1
Changxun Wu 1
Weihang Jiang 1
Vasily Tarasov 1
Edmund Nightingale 1
Mark Huang 1
Clement Dickey 1
Jibin Wang 1
Sungjin Lee 1
Chunming Hu 1
Chundong Wang 1
Xiaoguang Liu 1
Lee Ward 1
Fan Yang 1
Weishinn Ku 1
Jonathan Strickland 1
Junseok Shim 1
Jianhong Lin 1
Dahlia Malkhi 1
Gokhan Memik 1
Kei Davis 1
Stephanie Jones 1
Douglas Santry 1
Ragib Hasan 1
David Holland 1
Geoffrey Voelker 1
Cezary Dubnicki 1
Michael Vrable 1
Alexandros Batsakis 1
Mansour Shafaei 1
Gerardo Pelosi 1
Avishay Traeger 1
Sungroh Yoon 1
Tsengyi Chen 1
Ian Adams 1
Chunghsien Wu 1
Geming Chiu 1
Sugata Ghosal 1
Kristof Roomp 1
Lisa Fleischer 1
Hong Zhu 1
Ruben Michel 1
Wonil Choi 1
Ajay Dholakia 1
Zoran Dimitrijević 1
Klaus Schauser 1
Nick Murphy 1
Dongin Shin 1
Evgenia Smirni 1
Yungfeng Lu 1
Sašo Tomažič 1
Chunho Ng 1
Randolph Wang 1
Philip Carns 1
Charles Bacon 1
Sriram Sankar 1
Keqin Li 1
Alysson Bessani 1
Miguel Correia 1
Dan Tsafrir 1
Austin Donnelly 1
Lars Bongo 1
Runhui Li 1
Chongfeng Hu 1
Jin Qian 1
Shaun Benjamin 1
Michael Kozuch 1
Hong Jiang 1
Yue Yang 1
Ao Ma 1
Sangwhan Moon 1
Dongkun Shin 1
Youngjin Kim 1
Zhonghong Ou 1
Rebecca Stones 1
Yao Sun 1
Yangwook Kang 1
Tom Friedetzky 1
Toni Cortes 1
Anthony Skjellum 1
Enhong Chen 1
Zhichao Li 1
Satoshi Sugahara 1
Rakesh Iyer 1
Jian Zhang 1
Matthias Grawinkel 1
Suresh Jagannathan 1
Dimitrios Gunopulos 1
Yizheng Jiao 1
Sara Foresti 1
Pierangela Samarati 1
Windsor Hsu 1
Jeanna Matthews 1
Jongmoo Choi 1
Hsinwen Wei 1
Hyungjong Shin 1
Mohammed Khatib 1
Sarita Adve 1
Michail Flouris 1
Chengkang Hsieh 1
Akshay Katta 1
Michael Stumm 1
David Quigley 1
Puja Gupta 1
Ellis Wilson 1
John Shalf 1
Dinh Tran 1
Ming Li 1
Lei Tian 1
Jon Elerath 1
Jiri Schindler 1
Gyudong Shim 1
Youngwoo Park 1
Qiang Cao 1
Seungwon Hwang 1
Navendu Jain 1
Lawrence You 1
Greg O'Shea 1
Min Xu 1
Pooja Deo 1
Shigui Qi 1
Sangsoo Park 1
Jingwei Li 1
Zachary Peterson 1
Kai Li 1
Thanumalayan Pillai 1
Samuel Lang 1
Mark Shaw 1
Xianghong Luo 1
Yan Li 1
Hyungju Cho 1
Taesun Chung 1
Priya Sehgal 1
Kai Li 1
Abhishek Rajimwale 1
Armando Fox 1
Andrew Huang 1
Mary Baker 1
Joseph Murray 1
Rahat Mahmood 1
Lianghong Xu 1
Lawrence Chiu 1
Jianwen Zhu 1
Shan Lu 1
Jiguang Wan 1
Benlong Zhang 1
Jinpeng Huai 1
Ren Wang 1
Slavisa Sarafijanovic 1
Julie Kim 1
Matthew Curry 1
Ming Wu 1
Wenguang Chen 1
Mohammed Alghamdi 1
Maithili Narasimha 1
Vincent Freeh 1
Nandan Tammineedi 1
Yang Wang 1
Chris Mason 1
Youyou Lu 1
Long Sun 1
Marianne Winslett 1
James Lentini 1
Guanlin Lu 1
Rick Coulson 1
Kuoyi Huang 1
Tsungtai Yeh 1
Eunki Kim 1
Angelos Bilas 1
Kevin Bowers 1
Guanying Wu 1
Ben Eckart 1
Sajib Kundu 1
Christos Karamanolis 1
Magnus Karlsson 1
Thomas Schwarz 1
Harikesavan Krishnan 1
Myoungsoo Jung 1
Yihua Zhang 1
Edward Chang 1
Ioan Stefanovici 1
Zardosht Kasheff 1
Ning Li 1
Hakim Weatherspoon 1
Anxiao(Andrew) Jiang 1
Nitin Garg 1
Hariharan Gopalakrishnan 1
Robert Latham 1
Robert Ross 1
John Lui 1
Yuxing Peng 1
Xuechen Zhang 1
Garth Gibson 1
Bruno Quaresma 1
Dushyanth Narayanan 1
Jason Flinn 1
Mathew Oldham 1
Garth Gibson 1
Emery Berger 1
Jacob Lorch 1
TingHao Cheng 1
Alexey Tumanov 1
Jack Sun 1
Ertem Esiner 1
Christopher Meyers 1
Rubao Lee 1
Wei Wang 1
Michael Mesnier 1
Jian Zhou 1
Alexander Thomasian 1
Ivan Popov 1
Zhiwei Sun 1
Ji Zhang 1
Masaaki Tanaka 1
Ying Lin 1
Tyler Simon 1
Jaemin Ryu 1
Yongdai Kim 1
Gerald Popek 1
Assaf Natanzon 1
Xiaojian Wu 1
Demetrios Zeinalipour-Yazti 1
Song Lin 1
Rachel Traylor 1
Surendar Chandra 1
Knut Grimsrud 1
Byeonggil Jeon 1
John Garrison 1
Deborah Estrin 1
Thanos Makatos 1
Kaushik Dutta 1
Xiaoyun Zhu 1
Jay Dave 1
Sai Huang 1
Avani Wildani 1
David Essary 1
Yongchiang Tay 1
Seungho Lim 1
Bradley Vander Zanden 1
Minghua Chen 1
Andromachi Hatzieleftheriou 1
Zhifeng Chen 1
Michael Abd-El-Malek 1
Michael Reiter 1
Garth Goodson 1
William Josephson 1
Sotirios Damouras 1
Brian Noble 1
James Megquier 1
Luis Bathen 1
Binny Gill 1
Aydan Yumerefendi 1
Philip Shilane 1
Grant Wallace 1
Kuei Sun 1
Hyojun Kim 1
Cristian Ungureanu 1
Nitin Gupta 1
João Paulo 1
Kushal Wadhwani 1
P Nagesh 1
Abhinav Sharma 1
Min Fu 1
You Zhou 1
Jingwei Ma 1
Gang Wang 1
Alberto Miranda 1
Sascha Effert 1
Mohit Saxena 1
Youshan Miao 1
Ming Chen 1
Rekha Pitchumani 1
Vana Kalogeraki 1
Thomas Talpey 1
Ankur Mittal 1
Phaneendra Reddy 1
Stefano Paraboschi 1
Di Ma 1
David Chambliss 1
Ramesh Govindan 1
Haim Helman 1
Deepak Ganesan 1
Yannis Klonatos 1
Manolis Marazakis 1
Jianqiang Luo 1
Alina Oprea 1
Lihao Xu 1
Marcus Jager 1
Ryan Peterson 1
Kenneth Sevcik 1
Mohammad Zubair 1
Feng Wang 1
Shuwen Gao 1
David Donofrio 1
KK Rao 1
Anthony Tung 1
Richard Spillane 1
Chihyuan Huang 1
Hong Jiang 1
Yinjin Fu 1
Qin Xin 1
James Plank 1
Peter Trifonov 1
PingYi Hsu 1
Jingning Liu 1
Muthian Sivathanu 1
Arvind Krishnamurthy 1
Sumeet Sobti 1
Junwen Lai 1
Yinlong Xu 1
Qian Chang 1
Fenghao Zhang 1
Nihat Altiparmak 1
Kushagra Vaid 1
Sejin Kwon 1
Tomer Hertz 1
David Flynn 1
Elie Krevat 1
Mike Qin 1
Kahwai Lee 1
Sarah Diesburg 1
Naeyoung Song 1
Yongseok Son 1
Junyao Li 1
Jihong Kim 1
Yusik Kim 1
Wentao Han 1
Amanpreet Mukker 1
Xunfei Jiang 1
Yupu Zhang 1
Sudharshan Vazhkudai 1
Dutch Meyer 1
Qian Wang 1
Alok Choudhary 1
Mais Nijim 1
Stefan Savage 1
John Esmet 1
Sabrina Vimercati 1
Darren Sawyer 1
Changhyun Park 1
Jaehyuk Cha 1
Ben Greenstein 1
Beomjoo Seo 1
Akshat Verma 1
Taokai Lam 1
Marina Blanton 1
Jai Menon 1
Rajiv Wickremesinghe 1
Udi Wieder 1
Qi Zhang 1
Kevin Greenan 1
Medha Bhadkamkar 1
Fernando Farfán 1
Adam Buchsbaum 1
Vesna Pavlović 1

Affiliation Paper Counts
Dankook University 1
Chungbuk National University 1
University of Bergamo 1
University of Electronic Science and Technology of China 1
Dongduk Women's University 1
Los Alamos National Laboratory 1
Kookmin University 1
Sungkyunkwan University 1
Universitat Politecnica de Catalunya 1
Harvard University 1
The University of British Columbia 1
Apple Computer 1
Dartmouth College 1
Qualcomm Incorporated 1
Earlham College 1
Indian Institute of Science 1
AT&T Inc. 1
Sun Microsystems 1
Imperial College London 1
University of Southern California, Information Sciences Institute 1
University of Denver 1
University of Washington, Seattle 1
The University of Tennessee System 1
Yonsei University 1
Beijing University of Posts and Telecommunications 1
Peter the Great St. Petersburg Polytechnic University 1
Tamkang University 1
University of Durham 1
New Jersey Institute of Technology 1
Politecnico di Milano 1
Dickinson College, Pittsburgh 1
University of California, Berkeley 1
IBM Haifa Labs 1
Complutense University of Madrid 1
Oracle Corporation 1
Inha University, Incheon 1
University of California, Santa Barbara 1
University of Northern Iowa 1
Salk Institute for Biological Studies 1
University of Cyprus 1, Inc. 1
Symantec Corporation 1
Barcelona Supercomputing Center 1
Ulsan National Institute of Science and Technology 1
Al Baha University 1
IBM, Netherlands 1
National Taichung University of Science and Technology 1
University of Texas at Arlington 2
Lawrence Berkeley National Laboratory 2
Cornell University 2
Sandia National Laboratories, New Mexico 2
Santa Clara University 2
National Taipei University of Technology 2
Stanford University 2
University of Pittsburgh 2
The College of William and Mary 2
Hongik University 2
National Tsing Hua University 2
University of Minho 2
University of Twente 2
University of Notre Dame 2
University of Virginia 2
Massachusetts Institute of Technology 2
University of Tokyo 2
University of Alabama at Birmingham 2
Ben-Gurion University of the Negev 2
New Mexico Institute of Mining and Technology 2
Rutgers, The State University of New Jersey 2
California Institute of Technology 2
Pohang University of Science and Technology 2
University of Belgrade 2
Harvard School of Engineering and Applied Sciences 2
Harvey Mudd College 3
Virginia Commonwealth University 3
University of Texas at San Antonio 3
IBM India Research Laboratory 3
Ohio State University 3
Yale University 3
Google Inc. 3
Northwestern University 3
Duke University 3
Purdue University 3
National Chiao Tung University Taiwan 3
University of Tennessee, Knoxville 3
National University of Singapore 3
Oak Ridge National Laboratory 3
National University of Defense Technology China 3
University Michigan Ann Arbor 3
University of Milan 3
Temple University 3
Korea Advanced Institute of Science & Technology 3
Microsoft Research Asia 3
Ewha Women's University 4
Ajou University 4
Samsung Electronics Co. Ltd. 4
University of Ioannina 4
North Carolina State University 4
Seagate Research 4
Louisiana State University 4
HP Labs 4
University of Massachusetts Amherst 4
Academia Sinica Taiwan 4
San Diego State University 4
New York University 4
The University of North Carolina at Chapel Hill 4
Northeastern University 4
University of Southern California 4
University of California, Riverside 4
University of Ljubljana 4
Koc University 5
NEC Laboratories America, Inc. 5
Foundation for Research and Technology-Hellas 5
University of California, Los Angeles 5
National Taiwan University of Science and Technology 5
Johannes Gutenberg University Mainz 5
Microsoft Research Cambridge 5
Universidade de Lisboa 5
University of California, Irvine 6
University of California, San Diego 6
Microsoft Corporation 6
Xiamen University 6
Beihang University 6
Johns Hopkins University 6
EMC Corporation 7
Pennsylvania State University 7
Argonne National Laboratory 7
University of Nebraska - Lincoln 7
University of Science and Technology of China 7
Nankai University 7
Wayne State University 8
Florida State University 8
Wuhan National Laboratory for Optoelectronics 8
IBM Almaden Research Center 8
Intel Corporation 9
Princeton University 9
Texas A and M University 10
Chinese University of Hong Kong 10
IBM Thomas J. Watson Research Center 10
Florida International University 10
Auburn University 12
University of Illinois at Urbana-Champaign 12
National Taiwan University 13
Date Storage Institute, A-Star, Singapore 13
IBM Zurich Research Laboratory 13
Carnegie Mellon University 16
Hanyang University 17
Seoul National University 21
University of Toronto 22
Microsoft Research 22
Tsinghua University 23
University of California, Santa Cruz 25
Huazhong University of Science and Technology 29
Stony Brook University 39
University of Wisconsin Madison 41

ACM Transactions on Storage (TOS)

Volume 13 Issue 3, September 2017  Issue-in-Progress
Volume 13 Issue 2, June 2017 Special Issue on MSST 2016 and Regular Papers
Volume 13 Issue 1, March 2017 Special Issue on USENIX FAST 2016 and Regular Papers

Volume 12 Issue 4, August 2016
Volume 12 Issue 3, June 2016
Volume 12 Issue 2, February 2016
Volume 12 Issue 1, February 2016 Special Issue on Massive Storage Systems and Technologies (MSST 2015)

Volume 11 Issue 4, November 2015 Special Issue USENIX FAST 2015
Volume 11 Issue 3, July 2015
Volume 11 Issue 2, March 2015
Volume 11 Issue 1, February 2015

Volume 10 Issue 4, October 2014 Special Issue on Usenix Fast 2014
Volume 10 Issue 3, July 2014
Volume 10 Issue 2, March 2014
Volume 10 Issue 1, January 2014

Volume 9 Issue 4, November 2013
Volume 9 Issue 3, August 2013
Volume 9 Issue 2, July 2013
Volume 9 Issue 1, March 2013

Volume 8 Issue 4, November 2012
Volume 8 Issue 3, September 2012
Volume 8 Issue 2, May 2012
Volume 8 Issue 1, February 2012
Volume 7 Issue 4, January 2012

Volume 7 Issue 3, October 2011
Volume 7 Issue 2, July 2011
Volume 7 Issue 1, June 2011
Volume 6 Issue 4, May 2011

Volume 6 Issue 3, September 2010
Volume 6 Issue 2, July 2010
Volume 6 Issue 1, March 2010

Volume 5 Issue 4, December 2009
Volume 5 Issue 3, November 2009
Volume 5 Issue 2, June 2009
Volume 5 Issue 1, March 2009
Volume 4 Issue 4, January 2009

Volume 4 Issue 3, November 2008
Volume 4 Issue 2, May 2008
Volume 4 Issue 1, May 2008
Volume 3 Issue 4, February 2008

Volume 3 Issue 3, October 2007
Volume 3 Issue 2, June 2007
Volume 3 Issue 1, March 2007

Volume 2 Issue 4, November 2006
Volume 2 Issue 3, August 2006
Volume 2 Issue 2, May 2006
Volume 2 Issue 1, February 2006

Volume 1 Issue 4, November 2005
Volume 1 Issue 3, August 2005
Volume 1 Issue 2, May 2005
Volume 1 Issue 1, February 2005
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