ACM Transactions on

Storage (TOS)

Latest Articles


As the popularity of NAND flash expands in arenas from embedded systems to high-performance computing, a high-fidelity understanding of its specific properties becomes increasingly important. Further, with the increasing trend toward multiple-die, multiple-plane architectures and high-speed... (more)

Can We Group Storage? Statistical Techniques to Identify Predictive Groupings in Storage System Accesses

Storing large amounts of data for different users has become the new normal in a modern distributed... (more)


Note from Editor-in-Chief

I am delighted to announce that ACM Transactions on Storage is now being indexed by Thomson-Reuters through the Science Citation Index Expanded (SciSearch), Journal Citation Reports/Science Edition, Current Contents/Engineering Computing and Technology. This is in recognition of the quality of the articles appearing in Transactions on Storage, and I would like to extend my gratitude and appreciation to all of authors who have made this possible. As the old adage says, "a rising tide raises all boats" and the increased visibility of Transactions on Storage increases the impact of your article, and will attract even higher quality articles to our journal.

I want to again thank all of the authors who have made this possible, and express my hope that the growing impact of Transactions on Storage will continue. Computer Science is now a mature discipline, and the importance of journal publication is increasing. By sending your best work to Transactions on Storage you contribute to the field and to the standing of this journal.

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Forthcoming Articles

H-Scale: A Fast Approach to Scale Disk Arrays via Hybrid Stripe Deployment

SWANS: An Inter-Disk Wear-Leveling Strategy for RAID-0 Structured SSD Arrays

Efficient Deduplication in a Distributed Primary Storage Infrastructure

A large amount of duplicate data typically exists across volumes of virtual machines in cloud computing infrastructures. Deduplication allows reclaiming these duplicates while improving the cost-effectiveness of large scale multi-tenant infrastructures. However, traditional archival and backup deduplication systems impose prohibitive storage overhead for virtual machines hosting latency-sensitive applications. Primary deduplication systems reduce such penalty but rely on special cluster filesystems, centralized components, or restrictive workload assumptions. Also, some of these systems reduce storage overhead by confining deduplication to off-peak periods that, may be scarce in a cloud environment. We present DEDIS, a dependable and fully-decentralized system that performs cluster-wide background deduplication of virtual machines primary volumes. DEDIS works on top of any unsophisticated storage backend, centralized or distributed, as long as it exports a basic shared block device interface. Also, DEDIS does not rely on data locality assumptions and incorporates novel optimizations for reducing deduplication overhead and increasing its reliability. The evaluation of DEDIS open-source prototype shows that minimal I/O overhead is achievable even when deduplication and intensive storage I/O are executed simultaneously. Also, our design scales out and allows collocating DEDIS components and virtual machines in the same servers thus, sparing the need of additional hardware.

Does RAID Improve Lifetime of SSD Arrays?

LDM: Log Disk Mirroring with Improved Performance and Reliability for SSD-based Disk Arrays

Economic forces, driven by the desire to introduce flash-based Solid State Drives (SSDs) into the high-end storage market have resulted in the hybrid storage systems in the Cloud. However, A single flash-based (SSD) can not satisfy the performance, reliability and capacity requirements of enterprise or HPC storage systems in the Cloud. While an array of SSDs organized in a RAID structure, such as RAID5, provides the potential for high storage capacity and bandwidth, the reliability and performance problems will likely result from the parity update operations. In this paper, we propose a Log Disk Mirroring scheme (short for LDM) to improve the performance and reliability of SSD-based disk arrays. LDM is a hybrid disk array architecture that consists of several SSDs and two hard disk drives (HDDs). Our prototype implementation of the LDM array and the performance evaluations show that LDM array significantly outperforms the pure SSD-based disk arrays by a factor of 20.4 on average, and outperforms HPDA by a factor of 5.0 on average. The reliability analysis shows that the MTTDL of the LDM array is 2.7 times and 1.7 times better than that of pure SSD-based disk arrays and HPDA disk array.

MultiLanes: Providing Virtualized Storage for OS-level Virtualization on Many Cores

OS-level virtualization is an efficient method for server consolidation. However, the sharing of kernel services among the co-located virtualized environments (VEs) incurs performance interference between each other. Especially, interference effects within the shared I/O stack would lead to severe performance degradations on many-core platforms incorporating fast storage technologies (e.g., non-volatile memories). This article presents MultiLanes, a virtualized storage system for OS-level virtualization on many cores. MultiLanes builds an isolated I/O stack on top of a virtualized storage device for each VE to eliminate contention on kernel data structures and locks between them, thus scaling them to many cores. Moreover, the overhead of storage device virtualization is tuned to be negligible so that MultiLanes can deliver competitive performance against Linux. Apart from scalability, MultiLanes also delivers flexibility and security to all the VEs, as the virtualized storage device allows each VE to run its own guest file system. The evaluation of our prototype system built for Linux container (LXC) on a 32-core machine with both a RAM disk and a flash-based SSD demonstrates MultiLanes scales much better than Linux in micro- and macro-benchmarks, bringing significant performance improvements.

Classifying Data to Reduce Long Term Data Movement in Shingled Write Disks

Improving Flash-based Disk Cache with Lazy Adaptive Replacement

Efficient Memory-mapped I/O on Fast Storage Device

In operating systems, memory-mapped I/O (mmio) is an important access method that maps data to a memory. When mmio is used, hot data reside in the memory and clod data do in storage device, and data placement depends on the virtual memory subsystem of the operating system. Since the performance of storage has a direct impact on the performance of mmio, it is widely expected that better storage will lead to better performance. However, the expectation is limited when fast storage is used because the virtual memory subsystem does not reflect the feature of fast storage. In this article, we examine the mmio path to determine the influence of fast storage. We find that the overhead of the virtual memory subsystem, negligible on the HDD, prevents applications from using the full performance of fast storage. We present several optimization techniques and modify the Linux kernel to implement those techniques. Experimental results show that our optimized mmio has up to 7x better performance that the original. Compared with a system that has enough memory to keep all data, we achieve 92% performance of the resource-rich system. This implies that our system can effectively extend the main memory with fast storage.

TrueErase: Leveraging an Auxiliary Data Path for Per-file Secure Deletion

One important aspect of privacy is the ability to securely delete sensitive data from electronic storage in such a way that it cannot be recovered; we call this action secure deletion. Short of physically destroying the entire storage medium, existing software secure-deletion solutions tend to be piecemeal at best  they may only work for one type of storage or file system, may force the user to delete all files instead of selected ones, may require the added complexities of encryption and key storage, may require extensive changes and additions to the computers operating system or storage firmware, and may not handle system crashes gracefully. We present TrueErase, a holistic secure-deletion framework for individual systems that contain sensitive data. Through design, implementation, verification, and evaluation on both a hard drive and NAND flash, TrueErase shows that it is possible to construct a per-file, secure-deletion framework that can accommodate different storage media and legacy file systems, require limited changes to legacy systems, and handle common crash scenarios. TrueErase can serve as a building block by cryptographic systems that securely delete information by erasing encryption keys. The overhead is dependent on spatial locality, number of sensitive files, and workload (computational- or I/O-bound).

Storage Workload Identification

Workload identification is an important problem for cloud providers to solve because 1) providers can leverage this information to co-locate similar workloads in order to make the system more predictable 2) providers can identify workloads and subsequently give guidance to the subscribers as to associated best practices for provisioning those workloads. Historically, people have identified workloads by looking at their read/write ratios, random/sequential ratios, block size and inter-arrival frequency. Researchers are aware that workload characteristics change over time and that one cannot just take a point in time view of a workload because that will incorrectly characterize workload behavior. Increasingly, manual detection of workload signature is becoming harder because 1) it is difficult for a human to detect a pattern, and 2) representing a workload signature by a tuple consisting of {\it average} values for each of the signature components leads to a large error. In this paper, we present workload signature detection and matching algorithm that is able to correctly identify workload signatures and match them with other similar workload signatures. We have tested our algorithm on nine different workloads generated using publicly available traces and on real customer workloads running in field to show robustness of our approach.

TrueErase: Leveraging an Auxiliary Data Path for Per-file Secure Deletion

One important aspect of privacy is the ability to securely delete sensitive data from electronic storage in such a way that it cannot be recovered; we call this action secure deletion. Short of physically destroying the entire storage medium, existing system-based secure-deletion solutions tend to be piecemeal at best  they may only work for one type of storage or file system, may force the user to delete all files instead of selected ones, may require the added complexities of encryption and key storage, may require extensive changes and additions to the computers operating system or storage firmware, and may not handle system crashes gracefully. NAND flash storage is particularly troublesome, because its storage firmware makes it nearly impossible for the operating system to securely delete files and/or verify that the files have indeed been erased. We present TrueErase, a holistic secure-deletion framework suitable for individual systems that contain sensitive data. Through its design, implementation, verification, and evaluation on both a hard drive and NAND flash, TrueErase shows that it is possible to construct a per-file, encryption-free, secure-deletion framework that can accommodate different storage media and legacy file systems, require limited changes to legacy systems, and handle common crash scenarios.

Internal Parallelism of Flash Memory based Solid State Drives

Flash memory based solid state drives (SSDs) have shown a great potential to change todays storage infrastructure fundamentally. A unique merit of an SSD is its internal parallelism. In this paper we present a comprehensive study on understanding and exploiting internal parallelism of SSDs for high-speed data processing. Through extensive experiments and thorough analysis, we find that exploiting internal parallelism can not only substantially improve I/O performance (e.g., 7.2x) but also lead to many surprising side effects and dynamics, which have a strong implication to system and application designers. Based on these findings, we also present a set of case studies in database management systems, a typical data-intensive application, and show that exploiting internal parallelism can substantially improve system performance, and in the meantime, it also changes the equation for optimizing application performance and calls for a careful reconsideration on various design choices.

Efficient Dynamic Provable Possession of Remote Data via Update Trees

Storage Workload Identification

A User-Friendly Log Viewer for Storage Systems

For customers with remote support, the system collects and transmits logs to a central enterprise repository, where these are monitored for alerts, problem forecasting and troubleshooting. Very large log files limit the interpretability for the support engineers. For an engineer, a large volume of log messages may not pose any problem. Often it is desired to present the log messages in a comprehensive manner where a person can view the important messages first and then go into details if required. In this paper, we present a user-friendly log viewer where we first hide the unimportant messages from the log file. Messages with low utility are considered inconsequential as their removal does not impact the end user for the aforesaid purpose such as problem forecasting or troubleshooting. We relate the utility of a message to the probability of its appearance in the due context. We present machine learning based techniques that computes the usefulness of individual messages in a log file. (30% to 55%), with minimal error rates ( 7% to 20%). When limited user feedback is available, we show modifications to the technique to learn the user intent and accordingly further reduce the error.


Publication Years 2005-2016
Publication Count 186
Citation Count 1167
Available for Download 186
Downloads (6 weeks) 1530
Downloads (12 Months) 14003
Downloads (cumulative) 125499
Average downloads per article 675
Average citations per article 6
First Name Last Name Award

First Name Last Name Paper Counts
Andrea Arpaci-Dusseau 9
Youjip Won 6
Teiwei Kuo 6
Erez Zadok 6
Remzi Arpaci-Dusseau 5
Ethan Miller 5
Raju Rangaswami 4
Bianca Schroeder 4
Remzi Arpaci-Dusseau 4
Jiwu Shu 4
Charles Wright 4
Randal Burns 4
Weimin Zheng 4
Xiao Qin 4
James Cipar 3
Stergios Anastasiadis 3
Jenwei Hsieh 3
Song Jiang 3
Vijayan Prabhakaran 3
Guangyan Zhang 3
Lipin Chang 3
Ilias Iliadis 3
Heonyoung Yeom 3
Yuanyuan Zhou 3
Narasimha Reddy 3
Hyokyung Bahn 3
Ohad Rodeh 3
Nitin Agrawal 3
Hyeonsang Eom 3
Lidong Zhou 3
Dan Feng 3
Youngjin Yu 3
Hong Jiang 3
Darrell Long 3
Yuanhao Chang 3
Cheng Chen 2
Tao Xie 2
Jeffrey Chase 2
Erik Riedel 2
Taeho Hwang 2
Jaemin Jung 2
Eunji Lee 2
Scott Brandt 2
Wei Xue 2
Evangelos Eleftheriou 2
Xiaoyu Hu 2
Changsheng Xie 2
Kiran Muniswamy-Reddy 2
Nikolai Joukov 2
Chinhsien Wu 2
Ashvin Goel 2
Mark Storer 2
Suzhen Wu 2
Fred Douglis 2
Shu Yin 2
Gopalan Sivathanu 2
Alma Riska 2
Mahmut Kandemir 2
Daniel Fryer 2
Angela Brown 2
Arkady Kanevsky 2
Qingsong Wei 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
An Wang 2
Chris Dragga 2
Sudhanva Gurumurthi 2
Jianxi Chen 2
Peter Desnoyers 2
Ali Tosun 2
Xiaojun Ruan 2
Eitan Bachmat 2
Jongmin Gim 2
Peter Reiher 2
Geoff Kuenning 2
Zhenmin Li 2
John MacCormick 2
Kyuho Park 2
André Brinkmann 2
Swaminathan Sundararaman 2
Sriram Subramanian 2
Dongin Shin 2
Lei Tian 2
Bo Mao 2
Patrick Lee 2
Gregory Ganger 2
Roger Zimmermann 2
Jason Flinn 1
Kevin Bowers 1
Sajib Kundu 1
Guanying Wu 1
Xubin He 1
Ben Eckart 1
Christos Karamanolis 1
Magnus Karlsson 1
Thomas Schwarz 1
Harikesavan Krishnan 1
Xuechen Zhang 1
Robert Latham 1
Robert Ross 1
John Lui 1
Yuxing Peng 1
Alexey Tumanov 1
Jack Sun 1
Dushyanth Narayanan 1
Edward Chang 1
Mingdi Xue 1
Marianne Winslett 1
James Lentini 1
Anxiao(Andrew) Jiang 1
Nitin Garg 1
Garth Gibson 1
Darrell Long 1
Bruno Quaresma 1
Ji Zhang 1
William Jannen 1
Amogh Akshintala 1
Rachel Traylor 1
Surendar Chandra 1
Alexander Thomasian 1
Ivan Popov 1
Zhiwei Sun 1
Aydan Yumerefendi 1
Masaaki Tanaka 1
Ying Lin 1
Tyler Simon 1
Jaemin Ryu 1
Yongdai Kim 1
Assaf Natanzon 1
Xiaojian Wu 1
John Garrison 1
Knut Grimsrud 1
Luis Bathen 1
Binny Gill 1
Gerald Popek 1
Demetrios Zeinalipour-Yazti 1
Song Lin 1
Byeonggil Jeon 1
Philip Shilane 1
Grant Wallace 1
Kuei Sun 1
Hyojun Kim 1
Cristian Ungureanu 1
Avani Wildani 1
Deborah Estrin 1
Thanos Makatos 1
William Josephson 1
Brian Noble 1
Sotirios Damouras 1
Kaushik Dutta 1
Xiaoyun Zhu 1
Jay Dave 1
Nitin Gupta 1
Garth Goodson 1
David Essary 1
Yongchiang Tay 1
James Megquier 1
Bradley Vander Zanden 1
Jun Yang 1
Minghua Chen 1
Andromachi Hatzieleftheriou 1
Seungho Lim 1
Zhifeng Chen 1
Michael Abd-El-Malek 1
Michael Reiter 1
Youshan Miao 1
Ming Chen 1
Leif Walsh 1
Martín Farach-Colton 1
Ankur Mittal 1
Prashant Pandey 1
Phaneendra Reddy 1
Stefano Paraboschi 1
Alberto Miranda 1
Sascha Effert 1
Mohit Saxena 1
Michael Swift 1
Vinodh Venkatesan 1
Haim Helman 1
David Chambliss 1
Michael Bender 1
Abutalib Aghayev 1
Ao Ma 1
Mark Chamness 1
Leo Arulraj 1
Einar Mykletun 1
Mahesh Balakrishnan 1
Weikeng Liao 1
Deepak Bobbarjung 1
Josef Bacik 1
Seokhei Cho 1
Sooyong Kang 1
Walid Najjar 1
Pieter Hartel 1
Lars Nagel 1
Windsor Hsu 1
John Heidemann 1
Xiaodong Li 1
Kimberly Keeton 1
Di Ma 1
Vana Kalogeraki 1
David Donofrio 1
Deepak Ganesan 1
Ramesh Govindan 1
Yannis Klonatos 1
Manolis Marazakis 1
David Flynn 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
Yinlong Xu 1
Qian Chang 1
Elie Krevat 1
Mike Qin 1
Kahwai Lee 1
Tomer Hertz 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
Sam Noh 1
Peter Trifonov 1
Thomas Talpey 1
PingYi Hsu 1
Muthian Sivathanu 1
Sumeet Sobti 1
Junwen Lai 1
Arvind Krishnamurthy 1
Fenghao Zhang 1
Nihat Altiparmak 1
Kushagra Vaid 1
Sejin Kwon 1
Wentao Han 1
Amanpreet Mukker 1
Xunfei Jiang 1
Jun Yuan 1
John Esmet 1
Kaushik Veeraraghavan 1
Phillipa Gill 1
Ricardo Koller 1
Lingfang Zeng 1
Chandramohan Thekkath 1
Kirsten Hildrum 1
Philip Yu 1
Seonho Kim 1
Ron Arnan 1
Zhen Huang 1
Hyojun Kim 1
Lakshmi Bairavasundaram 1
David Wagner 1
Eno Thereska 1
Dilma Silva 1
Ahmed Amer 1
Haifeng Yu 1
Venugopalan Ramasubramanian 1
James Plank 1
Jin Li 1
Radu Sion 1
Anton Kos 1
Veljko Milutinovic 1
Xiao Qin 1
Nguyen Tran 1
Frank Chiang 1
Yulai Xie 1
Sabrina Vimercati 1
Darren Sawyer 1
Mark Corner 1
John Douceur 1
Nitin Agrawal 1
Yupu Zhang 1
Dutch Meyer 1
Sudharshan Vazhkudai 1
Qian Wang 1
Alok Choudhary 1
Mais Nijim 1
Krishna Kant 1
Changhyun Park 1
Jaehyuk Cha 1
Charles Weddle 1
Ben Greenstein 1
Beomjoo Seo 1
Akshat Verma 1
Taokai Lam 1
Liping Xiang 1
Mingqiang Li 1
Sangeetha Seshadri 1
Antony Rowstron 1
Jai Menon 1
Gordon Hughes 1
Daniel Ellard 1
Rajiv Wickremesinghe 1
Udi Wieder 1
Qi Zhang 1
Kevin Greenan 1
Medha Bhadkamkar 1
Fernando Farfán 1
Adam Buchsbaum 1
Marshall Mckusick 1
Yankit Li 1
Stefan Savage 1
Vesna Pavlović 1
Hyeongseog Kim 1
Robert Haas 1
Kristal Pollack 1
Jehoshua Bruck 1
Feng Zheng 1
Matthew Wachs 1
Karan Sanghi 1
Fernando André 1
Paulo Sousa 1
Kanchi Gopinath 1
Kaiwei Li 1
Bradley Kuszmaul 1
Asim Kadav 1
Xiaosong Ma 1
Stephen Scott 1
Hyungkyu Chang 1
Sukwoo Kang 1
Sheng Qiu 1
Sanjeev Trika 1
Soyoon Lee 1
Debra Hensgen 1
Tsansheng Hsu 1
Weikuan Shih 1
Mark Huang 1
Vasily Tarasov 1
Edmund Nightingale 1
Pochun Huang 1
Picheng Hsiu 1
Rohit Jain 1
Joel Wolf 1
Kevin Harms 1
William Allcock 1
Yubiao Pan 1
Ernst Biersack 1
Clement Dickey 1
Weihang Jiang 1
Phung Huynh 1
Amin Vahdat 1
Aichun Pang 1
Junfeng Yang 1
Jaewoo Choi 1
Ningfang Mi 1
Vagelis Hristidis 1
Cheng Huang 1
Gaewon You 1
Changxun Wu 1
Hyunjin Choi 1
Jasna Milovanovic 1
Jaka Sodnik 1
Sara Stancin 1
Jianzhong Huang 1
Jinyang Li 1
Jiwu Shu 1
Fan Yang 1
Weishinn Ku 1
Yang Zhan 1
Mansour Shafaei 1
Gerardo Pelosi 1
Jianhong Lin 1
Dahlia Malkhi 1
Jonathan Strickland 1
Junseok Shim 1
Gokhan Memik 1
Cezary Dubnicki 1
Kei Davis 1
Avishay Traeger 1
Sungroh Yoon 1
Tsengyi Chen 1
Jin Qian 1
Douglas Santry 1
Shaun Benjamin 1
Wonil Choi 1
Ian Adams 1
Chunghsien Wu 1
Geming Chiu 1
Lars Bongo 1
Sugata Ghosal 1
Kristof Roomp 1
Lisa Fleischer 1
Hong Zhu 1
Ruben Michel 1
Philip Carns 1
Charles Bacon 1
Runhui Li 1
Michael Kozuch 1
Chongfeng Hu 1
Austin Donnelly 1
Dan Tsafrir 1
Ajay Dholakia 1
Zoran Dimitrijević 1
Klaus Schauser 1
Nick Murphy 1
Dongin Shin 1
Evgenia Smirni 1
Kaladhar Voruganti 1
Ao Ma 1
Lanyue Lu 1
Chunho Ng 1
Ragib Hasan 1
David Holland 1
Michael Vrable 1
Geoffrey Voelker 1
Alexandros Batsakis 1
Yungfeng Lu 1
Lee Ward 1
Sašo Tomažič 1
Randolph Wang 1
Sriram Sankar 1
Alysson Bessani 1
Miguel Correia 1
Keqin Li 1
Enhong CHEN 1
Zhichao Li 1
Rob Johnson 1
Donald Porter 1
Yizheng Jiao 1
Sara Foresti 1
Pierangela Samarati 1
Windsor Hsu 1
Yao Sun 1
Yangwook Kang 1
Tom Friedetzky 1
Toni Cortes 1
Anthony Skjellum 1
Satoshi Sugahara 1
Rakesh Iyer 1
Jian Zhang 1
Suresh Jagannathan 1
Jeanna Matthews 1
Jongmoo Choi 1
Hsinwen Wei 1
Dimitrios Gunopulos 1
Mohammed Khatib 1
Hyungjong Shin 1
Matthias Grawinkel 1
Rahat Mahmood 1
Ellis Wilson 1
John Shalf 1
Sarita Adve 1
Michail Flouris 1
Priya Sehgal 1
Kai Li 1
Abhishek Rajimwale 1
Michael Swift 1
Chengkang Hsieh 1
Akshay Katta 1
Michael Stumm 1
David Quigley 1
Puja Gupta 1
Samuel Lang 1
Lawrence Chiu 1
Lianghong Xu 1
Mary Baker 1
Dinh Tran 1
Armando Fox 1
Andrew Huang 1
Feng Chen 1
Ming Li 1
Lei Tian 1
Jon Elerath 1
Jiri Schindler 1
Gyudong Shim 1
Youngwoo Park 1
Qiang Cao 1
Shan Lu 1
Min Xu 1
Seungwon Hwang 1
Navendu Jain 1
Joseph Murray 1
Lawrence You 1
Zachary Peterson 1
Kai Li 1
Mark Shaw 1
Xianghong Luo 1
Yan Li 1
Hyungju Cho 1
Taesun Chung 1
Ming Wu 1
Wenguang Chen 1
Mohammed Alghamdi 1
Guanlin Lu 1
Matthew Curry 1
Garth Gibson 1
Emery Berger 1
Jacob Lorch 1
Maithili Narasimha 1
Yang Wang 1
Vincent Freeh 1
Nandan Tammineedi 1
Chris Mason 1
Kuoyi Huang 1
Tsungtai Yeh 1
Sam Noh 1
Rick Coulson 1
Mathew Oldham 1
Eunki Kim 1
Youyou Lu 1
Long Sun 1
TingHao Cheng 1
Myoungsoo Jung 1
Angelos Bilas 1

Affiliation Paper Counts
Barcelona Supercomputing Center 1
Massachusetts Institute of Technology 1
Sun Microsystems 1, Inc. 1
University of Cyprus 1
Los Alamos National Laboratory 1
Salk Institute 1
Indian Institute of Science 1
Earlham College 1
Rutgers University 1
Temple University 1
University of California, Santa Barbara 1
Yale University 1
Oracle Corporation 1
Complutense University of Madrid 1
University of Bergamo 1
Dartmouth College 1
National Taichung Institute of Technology Taiwan 1
The University of North Carolina at Chapel Hill 1
University of California, Berkeley 1
Dickinson College, Pittsburgh 1
Politecnico di Milano 1
New Jersey Institute of Technology 1
University of Southern California, Information Sciences Institute 1
University of Denver 1
University of Washington Seattle 1
Dankook University 1
Al Baha University 1
The University of Tennessee System 1
Yonsei University 1
Saint Petersburg State Polytechnical University 1
Universitat Politecnica de Catalunya 1
Cornell University 1
Tamkang University 1
Harvard University 1
The University of British Columbia 1
Santa Clara University 1
University of Durham 1
Symantec Corporation 1
Harvard School of Engineering and Applied Sciences 2
University of Belgrade 2
Pohang University of Science and Technology 2
California Institute of Technology 2
New Mexico Institute of Mining and Technology 2
Ben-Gurion University of the Negev 2
University of Alabama at Birmingham 2
University of Tokyo 2
University of Virginia 2
University of Twente 2
San Diego State University 2
National Tsing Hua University 2
Hongik University 2
The College of William and Mary 2
University of Pittsburgh 2
Sandia National Laboratories 2
Stanford University 2
National Taipei University of Technology 2
Lawrence Berkeley National Laboratory 2
Google Inc. 2
Ohio State University 2
Virginia Commonwealth University 2
Harvey Mudd College 2
Ewha Women's University 2
Ajou University 3
University of Texas at San Antonio 3
IBM India Research Laboratory New Delhi 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
Xiamen University 3
Oak Ridge National Laboratory 3
National University of Defense Technology China 3
University Michigan Ann Arbor 3
University of Milan 3
Korea Advanced Institute of Science & Technology 3
Microsoft Research Cambridge 3
Microsoft Research Asia 3
HP Labs 4
University of California, Riverside 4
New York University 4
University of Southern California 4
University of Ljubljana 4
Seagate Research 4
Samsung Electronics 4
University of Ioannina 4
North Carolina State University 4
University of Massachusetts Amherst 4
Northeastern University 4
Academia Sinica Taiwan 4
IBM Almaden Research Center 5
Florida State University 5
Foundation for Research and Technology-Hellas 5
University of Lisbon 5
NEC Laboratories America, Inc. 5
Johannes Gutenberg University Mainz 5
University of California, Los Angeles 5
National Taiwan University of Science and Technology 5
Microsoft 6
University of California, San Diego 6
Johns Hopkins University 6
Date Storage Institute, A-Star, Singapore 6
University of California, Irvine 6
University of Science and Technology of China 7
EMC Corporation 7
Pennsylvania State University 7
Intel Corporation 7
Argonne National Laboratory 7
University of Nebraska - Lincoln 7
Chinese University of Hong Kong 8
Texas A and M University 8
Wayne State University 8
IBM Zurich Research Laboratory 9
Princeton University 9
IBM Thomas J. Watson Research Center 10
Florida International University 10
University of Illinois at Urbana-Champaign 12
Huazhong University of Science and Technology 12
Auburn University 12
National Taiwan University 13
Carnegie Mellon University 16
Seoul National University 17
Hanyang University 17
University of Toronto 19
University of California, Santa Cruz 20
Microsoft Research 22
Tsinghua University 23
Stony Brook University 35
University of Wisconsin Madison 36

ACM Transactions on Storage (TOS)

Volume 12 Issue 2, February 2016  Issue-in-Progress
Volume 12 Issue 1, January 2016  Issue-in-Progress

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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