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Ouroboros Wear Leveling for NVRAM Using Hierarchical Block Migration

Emerging nonvolatile RAM (NVRAM) technologies have a limit on the number of writes that can be made to any cell, similar to the erasure limits in NAND... (more)

Experience from Two Years of Visualizing Flash with SSDPlayer

Data visualization is a thriving field of computer science, with widespread impact on diverse scientific disciplines, from medicine and meteorology to... (more)

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

To design the write buffer and flash translation layer (FTL) for a solid-state drive (SSD), previous studies have tried to increase overall SSD performance by parallel I/O and garbage collection overhead reduction. Recent works have proposed pattern-based managements, which uses the request size and read- or write-intensiveness to apply different... (more)

Optimal Repair Layering for Erasure-Coded Data Centers: From Theory to Practice

Repair performance in hierarchical data centers is often bottlenecked by cross-rack network transfer. Recent theoretical results show that the... (more)

CosaFS: A Cooperative Shingle-Aware File System

In this article, we design and implement a cooperative shingle-aware file system, called CosaFS, on heterogeneous storage devices that mix solid-state drives (SSDs) and shingled magnetic recording (SMR) technology to improve the overall performance of storage systems. The basic idea of CosaFS is to classify objects as hot or cold objects based on a... (more)

TinyLFU: A Highly Efficient Cache Admission Policy

This article proposes to use a frequency-based cache admission policy in order to boost the effectiveness of caches subject to skewed access distributions. Given a newly accessed item and an eviction candidate from the cache, our scheme decides, based on the recent access history, whether it is worth admitting the new item into the cache at the... (more)

Client-Side Journaling for Durable Shared Storage

Hardware consolidation in the datacenter often leads to scalability bottlenecks from heavy utilization of critical resources, such as the storage and... (more)

GCMix: An Efficient Data Protection Scheme against the Paired Page Interference

In multi-level cell (MLC) NAND flash memory, two logical pages are overlapped on a single physical page. Even after a logical page is programmed, the data can be corrupted if the programming of the coexisting logical page is interrupted. This phenomenon is called paired page interference. This article proposes a novel software technique to deal... (more)

NEWS

  • 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
OrcFS: Orchestrated File System for Flash Storage

We develop OrcFS, Orchestrated File System for Flash storage. It vertically integrates the log-structured file system and the Flash-based storage device eliminating all the redundancies across the layers. A few modern file systems adopt sophisticated append-only data structures to manage its space in an effort to optimize the behavior with respect to the append-only nature of the Flash medium. While the benefit of adopting append-only data structure seems to be fairly promising, it makes the stack of software layers full of unnecessary redundancies which leaves substantial room for improvement. The redundancies include (i) redundant levels of indirection (address translation) , (ii) duplicate efforts to reclaim the invalid blocks which is called segment cleaning and garbage collection in the file system layer and in the storage device, respectively, and (iii) excessive over-provisioning, i.e. separate over-provisioning areas in each layer. OrcFS eliminates the redundancies via distributing the address translation, segment cleaning (or garbage collection), bad block management, and wear-leveling across the layers. OrcFS reduces the device mapping table requirement to 1/465 against the page mapping and removes 1/4 of the write volume under heavy random write workload. In varmail, OrcFS achieves 56% performance gain against EXT4.

GDS-LC: A Latency and Cost Aware Client Caching for Cloud Storage

Successfully integrating cloud storage as a primary storage layer in the I/O stack is highly challenging due to the two inherent critical issues  the high latency of cloud I/Os and the unconventional pricing model of cloud storage. Caching is a crucial technology to minimize the latency and price of cloud I/Os. Unfortunately, the current cloud caching schemes are designed by adopting miss reduction as the sole objective, while ignoring the fact that various cache misses could have distinct actual effects in term of latency and monetary cost. In this paper, we present a cost-aware caching scheme specifically designed for cloud storage, called GDS-LC. The proposed scheme offers a comprehensive cache design by considering not only the access locality but also the associated latency and price. With GDS-LC, we can effectively filter out the high-latency and high-price cloud I/Os and thus successfully reshape the cloud I/O streams to the desired low-latency and low-cost pattern. We have built a prototype to emulate a typical cloud client cache and evaluate the GDS-LC scheme with Amazon Simple Storage Services (S3) in three different scenarios, local cloud, Internet cloud, and heterogeneous cloud. Our experimental results show very promising results.

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

A Low-cost Disk Solution Enabling LSM-tree to Achieve High Performance for Mixed Read/Write Workloads

LSM-tree has been widely used in data management production systems for write-intensive workloads. However, as read and write workloads co-exist under LSM-tree, data accesses can experience long latency and low throughput due to the interferences to buffer caching from the compaction, a major and frequent operation in LSM-tree. After a compaction, the existing data blocks are reorganized and written to other locations on disks. As a result, the related data blocks that have been loaded in the buffer cache are invalidated since their referencing addresses are changed, causing serious performance degradations. In order to re-enable high-speed buffer caching during intensive writes, we propose Log-Structured buffered-Merge tree (simplified as LSbM-tree) by adding a compaction buffer on disks, to minimize the cache invalidations on buffer cache caused by compactions. The compaction buffer efficiently and adaptively maintains the frequently visited data sets. In LSbM, strong locality objects can be effectively kept in the buffer cache with minimum or without harmful invalidations. With the help of a small on-disk compaction buffer, LSbM achieves a high query performance by enabling effective buffer caching, while retaining all the merits of LSM-tree for write-intensive data processing, and providing high bandwidth of disks for range queries.

Characterizing 3D Floating Gate NAND Flash: Observations, Analyses and Implications

Since both NAND flash memory manufacturers and users are turning their attentions from planar architecture towards 3D architecture, it becomes more critical and urgent to have a strong understanding of the characteristics of 3D NAND flash memory. These characteristics, especially those different from planar NAND flash, are the foundations of efficient flash management. In this paper, we characterize a state-of-the-art 3D floating gate NAND flash memory through comprehensive experiments on an FPGA-based 3D NAND flash evaluation platform. Then, we present distinct observations on its performance and reliability, such as operation latencies and various error patterns, and carefully analyze them from physical and circuit-level perspectives. Although 3D NAND flash provides much higher storage densities than planar NAND flash, it faces new performance challenges in garbage collection overhead and program performance variation, and more complicated reliability issues due to e.g., distinct location dependence and value dependence of errors. We also summarize the differences of 3D NAND flash from planar NAND flash and discuss design implications on flash memory management brought by the architecture innovation. We believe that our work will facilitate developing novel 3D NAND flash-oriented designs to achieve better performance and reliability.

Challenges and Solutions for Tracing Storage Systems: A Case Study with Spectrum Scale

IBM Spectrum Scale's parallel file system General Parallel File System (GPFS) has a 20-year development history with over 100 contributing developers. Its ability to support strict POSIX semantics across more than 10K clients leads to a complex design with intricate interactions between the cluster nodes. Tracing has proven to be a vital tool to understand the behavior and the anomalies of such a complex software product. However, the necessary trace information is often buried in hundreds of gigabytes of byproduct trace records. Further, the overhead of tracing can significantly impact running applications and file system performance, limiting the use of tracing in a production system. In this article, we discuss the evolution of the mature and highly scalable GPFS tracing tool and describe the process of designing GPFS' new tracing interface, FlexTrace, which allows developers and users to accurately specify what to trace for the problem they are trying to solve. We evaluate our methodology and prototype, demonstrating that the proposed approach has negligible overhead even under intensive I/O workloads.

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.

hfplayer: Scalable Reply for Intensive Block I/O Workloads

We introduce new methods to replay intensive block I/O workloads more accurately. These methods canbe used to reproduce realistic workloads for benchmarking, performance validation, and tuning of a high-performance block storage device/system. In this paper, we study several sources in the stock operating system that introduce uncertainty in the workload replay. Based on the remedies of these findings, we design and develop a new replay tool called hfplayer that replay intensive block I/O workloads in a similar unscaled environment with more accuracy. To replay a given workload trace in a scaled environment with faster storage or host server, the dependency between I/O requests becomes crucial since the timing and ordering of I/O requests is expected to change according to these dependencies. Therefore, we propose a heuristic way of speculating I/O dependencies in a block I/O trace. Using the generated dependency graph, hfplayer tries to propagate I/O related performance gains appropriately along the I/O dependency chains and mimics original application behavior when it executes in a scaled environment. We evaluate hfplayer with a wide range of workloads using several accuracy metrics and find that it produces better accuracy when compared with other replay approaches.

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.

Workload Characterization for Enterprise Disc Drives

Abstract  The paper presents an analysis of drive workloads from enterprise storage systems. The drive workloads are obtained from field return units from a cross-section of enterprise storage system vendors and thus provides a view of the workload characteristics over a wide spectrum of end-user applications. The workload parameters that have been characterized include transfer lengths, access patterns, locality and throughput. The study shows that reads are the dominant workload accounting for 80% of the accesses to the drive. Writes are dominated by short block random accesses while reads range from random to highly sequential. A trend analysis over the period 2010-2014 shows that the workload has remained fairly constant even as the capacities of the drives shipped has steadily increased. The study shows that the data stored on disk drives is relatively cold  on average less than 4% of the drive capacity is accessed in a given 2 hour interval.

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.

Introduction to the Special Issue on MSST 2017

Bibliometrics

Publication Years 2005-2017
Publication Count 239
Citation Count 1618
Available for Download 239
Downloads (6 weeks) 1769
Downloads (12 Months) 13828
Downloads (cumulative) 149383
Average downloads per article 625
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)
Haryadi S Gunawi ACM Doctoral Dissertation Award
Honorable Mention (2009) ACM Doctoral Dissertation Award
Honorable Mention (2009)
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)
SAM H. NOH ACM Distinguished Member (2017)
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)
Roger Zimmermann ACM Distinguished Member (2017)

First Name Last Name Paper Counts
Andrea Arpaci-Dusseau 12
Remzi Arpaci-Dusseau 8
Erez Zadok 7
Dan Feng 7
Youjip Won 6
Teiwei Kuo 6
Ethan Miller 6
Bianca Schroeder 5
Hong Jiang 5
Geoff Kuenning 5
Hyokyung Bahn 5
Cheng Chen 4
Raju Rangaswami 4
Remzi Arpaci-Dusseau 4
Changsheng XIE 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
Hyeonsang Eom 3
Eunji Lee 3
Youngjin Yu 3
Darrell Long 3
Suzhen Wu 3
Stergios Anastasiadis 3
Lanyue Lu 3
Yuanhao Chang 3
Jenwei Hsieh 3
Song Jiang 3
Fred Douglis 3
Feng Chen 3
James Cipar 3
Vijayan Prabhakaran 3
An Wang 3
Lipin Chang 3
Guangyan Zhang 3
Samhyuk Noh 3
Yuanyuan Zhou 3
Xubin He 3
Jianxi Chen 3
Nitin Agrawal 3
Ohad Rodeh 3
Bo Mao 3
Patrick Lee 3
Eitan Bachmat 2
Peter Desnoyers 2
William Jannen 2
Amogh Akshintala 2
Zhenmin Li 2
John MacCormick 2
Philip Shilane 2
Grant Wallace 2
Ramnatthan Alagappan 2
Kyuho Park 2
Jun Yang 2
Binbing Hou 2
André Brinkmann 2
Ming Chen 2
Swaminathan Sundararaman 2
Sriram Subramanian 2
Martín Farach-Colton 2
Leif Walsh 2
Prashant Pandey 2
Vinodh Venkatesan 2
Lei Tian 2
Dongin Shin 2
Gregory Ganger 2
Jun Yuan 2
Roger Zimmermann 2
Qing Liu 2
Tao Xie 2
Mahesh Balakrishnan 2
Erik Riedel 2
Ahmed Amer 2
Michael Bender 2
Taeho Hwang 2
Jaemin Jung 2
Yuchong Hu 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
Ashvin Goel 2
Shu Yin 2
Michael Swift 2
Gopalan Sivathanu 2
Alma Riska 2
Mahmut Kandemir 2
Arkady Kanevsky 2
Rob Johnson 2
Donald Porter 2
Thanumalayan Pillai 2
Zhan Shi 2
Daniel Fryer 2
Angela Brown 2
Jayanta Basak 2
Mario Blaum 2
Adam Manzanares 2
Gene Tsudik 2
Bo Hong 2
William Bolosky 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
Lingfang Zeng 1
Chandramohan Thekkath 1
Kirsten Hildrum 1
Philip YU 1
Seonho Kim 1
Ron Arnan 1
Zhaoguo Wang 1
Haibing Guan 1
Binyu Zang 1
Henry Nelson 1
Carl Waldspurger 1
Haryadi Gunawi 1
Venugopalan Ramasubramanian 1
Fei Wu 1
Ping Huang 1
Jingui Wang 1
Junjie Ren 1
Jin Li 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
Dilma Silva 1
Lakshmi Bairavasundaram 1
Kimberly Keeton 1
Kaushik Veeraraghavan 1
Phillipa Gill 1
Ricardo Koller 1
Haifeng Yu 1
Hyojun Kim 1
Robert Hall 1
Adilet Kachkeev 1
Samuel Braunfeld 1
Alptekin Küpçü 1
Öznur Özkasap 1
James Plank 1
Windsor Hsu 1
Tianyu Wo 1
Tao Xie 1
Kaiwei Li 1
Asim Kadav 1
Xiaosong Ma 1
Sai Huang 1
Avani Wildani 1
David Essary 1
Yongchiang Tay 1
Seungho Lim 1
Bradley Vander Zanden 1
Andromachi Hatzieleftheriou 1
Min Fu 1
You Zhou 1
Jingwei Ma 1
Gang Wang 1
Minghua Chen 1
Zhifeng Chen 1
Michael Abd-El-Malek 1
Michael Reiter 1
Garth Goodson 1
William Josephson 1
Brian Noble 1
Sotirios Damouras 1
James Megquier 1
Aydan Yumerefendi 1
Nitin Gupta 1
João Paulo 1
Kushal Wadhwani 1
Luis Bathen 1
Binny Gill 1
Kuei Sun 1
Hyojun Kim 1
Cristian Ungureanu 1
P Nagesh 1
Abhinav Sharma 1
Alberto Miranda 1
Sascha Effert 1
Mohit Saxena 1
Youshan Miao 1
Vana Kalogeraki 1
Rekha Pitchumani 1
Thomas Talpey 1
Ankur Mittal 1
Phaneendra Reddy 1
Stefano Paraboschi 1
Di Ma 1
Ramesh Govindan 1
Yannis Klonatos 1
Manolis Marazakis 1
Jianqiang Luo 1
Alina Oprea 1
Randolph Wang 1
Philip Carns 1
Charles Bacon 1
Runhui Li 1
Sriram Sankar 1
Keqin Li 1
Alysson Bessani 1
Miguel Correia 1
Dan Tsafrir 1
Chongfeng Hu 1
Austin Donnelly 1
Lars Bongo 1
Michael Kozuch 1
Hong Jiang 1
Ao Ma 1
Sangwhan Moon 1
Yue Yang 1
Jin Qian 1
Shaun Benjamin 1
Dongkun Shin 1
Youngjin Kim 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
Suresh Jagannathan 1
Dimitrios Gunopulos 1
Matthias Grawinkel 1
Yizheng Jiao 1
Sara Foresti 1
Pierangela Samarati 1
Windsor Hsu 1
Jongmoo Choi 1
Hyungjong Shin 1
Sarita Adve 1
Michail Flouris 1
Hsinwen Wei 1
Mohammed Khatib 1
Jeanna Matthews 1
Chengkang Hsieh 1
Michael Stumm 1
David Quigley 1
Puja Gupta 1
Akshay Katta 1
Cheng Li 1
Farhaan Jalia 1
Mingzhe Hao 1
Rohit Singh 1
Travis Grusecki 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
Min Xu 1
Seungwon Hwang 1
Navendu Jain 1
Ren Wang 1
Slavisa Sarafijanovic 1
Julie Kim 1
Andrej Kos 1
Lawrence You 1
Greg O'Shea 1
Pooja Deo 1
Shigui Qi 1
Sangsoo Park 1
Jingwei Li 1
Zachary Peterson 1
Kai Li 1
Samuel Lang 1
Mark Shaw 1
Xianghong Luo 1
Yan Li 1
Hyungju Cho 1
Taesun Chung 1
Mary Baker 1
Priya Sehgal 1
Kai Li 1
Abhishek Rajimwale 1
Joseph Murray 1
Armando Fox 1
Andrew Huang 1
Lawrence Chiu 1
Lianghong Xu 1
Shan Lu 1
Jiguang Wan 1
Lihao Xu 1
Haim Helman 1
David Chambliss 1
Deepak Ganesan 1
Marcus Jager 1
Ryan Peterson 1
Kenneth Sevcik 1
Mohammad Zubair 1
Feng Wang 1
Yubin Xia 1
Marko Vukolić 1
Ram Kesavan 1
David Donofrio 1
Shuwen Gao 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
Yusik Kim 1
PingYi Hsu 1
Jingning Liu 1
Muthian Sivathanu 1
Sumeet Sobti 1
Junwen Lai 1
Arvind Krishnamurthy 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
Naeyoung Song 1
Yongseok Son 1
Junyao Li 1
Sarah Diesburg 1
Jihong Kim 1
Wentao Han 1
Amanpreet Mukker 1
Xunfei Jiang 1
Jianwen Zhu 1
Rahat Mahmood 1
Benlong Zhang 1
Jinpeng Huai 1
Matthew Curry 1
Ming Wu 1
Wenguang Chen 1
Mohammed Alghamdi 1
Maithili Narasimha 1
Yang Wang 1
Vincent Freeh 1
Nandan Tammineedi 1
Chris Mason 1
Youyou Lu 1
Long Sun 1
Marianne Winslett 1
James Lentini 1
Guanlin Lu 1
Kuoyi Huang 1
Eunki Kim 1
Angelos Bilas 1
Kevin Bowers 1
Tsungtai Yeh 1
Rick Coulson 1
Sajib Kundu 1
Guanying Wu 1
Ben Eckart 1
Christos Karamanolis 1
Magnus Karlsson 1
Thomas Schwarz 1
Harikesavan Krishnan 1
Mingkai Dong 1
Haibo Chen 1
Heng Zhang 1
Yihua Zhang 1
Myoungsoo Jung 1
Edward Chang 1
Michael Mesnier 1
Jian Zhou 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
Stephen Scott 1
Hyungkyu Chang 1
Sukwoo Kang 1
Sheng Qiu 1
Christina Strong 1
Soyoon Lee 1
Krishna Kant 1
Weikuan Shih 1
Tsansheng Hsu 1
Sanjeev Trika 1
Debra Hensgen 1
Pochun Huang 1
Picheng Hsiu 1
Rohit Jain 1
Joel Wolf 1
Dan Dobre 1
Vijay Chidambaram 1
Michaelhao Tong 1
Phung Huynh 1
Junfeng Yang 1
Hyunjin Choi 1
Jaewoo Choi 1
Aichun Pang 1
Ningfang Mi 1
Vagelis Hristidis 1
Gaewon You 1
Xiaoguang Liu 1
Chundong Wang 1
Jasna Milovanovic 1
Cheng Huang 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
Weihang Jiang 1
Vasily Tarasov 1
Edmund Nightingale 1
Amin Vahdat 1
Changxun Wu 1
Clement Dickey 1
Jibin Wang 1
Einar Mykletun 1
Leo Arulraj 1
Weikeng Liao 1
Deepak Bobbarjung 1
Josef Bacik 1
Walid Najjar 1
Radu Sion 1
Abutalib Aghayev 1
Lars Nagel 1
Mark Chamness 1
Ao Ma 1
Seokhei Cho 1
Sooyong Kang 1
John Heidemann 1
Xiaodong Li 1
Pieter Hartel 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
Garth Gibson 1
Emery Berger 1
Jacob Lorch 1
Alexey Tumanov 1
Jack Sun 1
Ertem Esiner 1
Christopher Meyers 1
Mathew Oldham 1
TingHao Cheng 1
Rubao Lee 1
Wei Wang 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
Byeonggil Jeon 1
Deborah Estrin 1
Thanos Makatos 1
John Garrison 1
Knut Grimsrud 1
Kaushik Dutta 1
Xiaoyun Zhu 1
Jay Dave 1
Paolo Viotti 1
Tianfeng Jiao 1
Shiqin Yan 1
Swaminatahan Sundararaman 1
Andrew Chien 1
Mark Huang 1
Sungjin Lee 1
Chunming Hu 1
Lee Ward 1
Fan Yang 1
Weishinn Ku 1
Dahlia Malkhi 1
Jianhong Lin 1
Jonathan Strickland 1
Junseok Shim 1
Gokhan Memik 1
Cezary Dubnicki 1
Kei Davis 1
Stephanie Jones 1
Ragib Hasan 1
David Holland 1
Alexandros Batsakis 1
Mansour Shafaei 1
Douglas Santry 1
Michael Vrable 1
Geoffrey Voelker 1
Gerardo Pelosi 1
Sungroh Yoon 1
Ian Adams 1
Chunghsien Wu 1
Geming Chiu 1
Tsengyi Chen 1
Avishay Traeger 1
Sugata Ghosal 1
Kristof Roomp 1
Lisa Fleischer 1
Hong Zhu 1
Ruben Michel 1
Dean Hildebrand 1
Wonil Choi 1
Ajay Dholakia 1
Aishwarya Ganesan 1
Zoran Dimitrijević 1
Klaus Schauser 1
Nick Murphy 1
Dongin Shin 1
Evgenia Smirni 1
Chunho Ng 1
Zhonghong Ou 1
Rebecca Stones 1
Yungfeng Lu 1
Sašo Tomažič 1
Yupu Zhang 1
Sudharshan Vazhkudai 1
Qian Wang 1
Alok Choudhary 1
Mais Nijim 1
Dutch Meyer 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
Geetika Bangera 1
Huaicheng Li 1
Yuvraj Patel 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
Yankit Li 1
Jens Jelitto 1
Sunjin Lee 1
Yuxiang Ma 1
Vesna Pavlović 1
Hyeongseog Kim 1
Robert Haas 1
Kristal Pollack 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
Gordon Hughes 1
Daniel Ellard 1
Mark Corner 1
John Douceur 1
Nitin Agrawal 1
Mingqiang Li 1
Sangeetha Seshadri 1
José Pereira 1
Xiaolan Chen 1
Hyuck Han 1
Marshall McKusick 1
Peng Xu 1
Antony Rowstron 1
Mark Stanovich 1
Charles Weddle 1
Junbin Kang 1
Ye Zhai 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
University of Texas at Austin 1
Earlham College 1
Indian Institute of Science, Bangalore 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
Amazon.com, Inc. 1
Symantec Corporation 1
Barcelona Supercomputing Center 1
Ulsan National Institute of Science and Technology 1
VMware, Inc 1
Al Baha University 1
IBM, Netherlands 1
National Taichung University of Science and Technology 1
Facebook, Inc. 1
NetApp, Germany 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
IBM, USA 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
NetApp, India 2
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
Harvey Mudd College 4
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
Johns Hopkins University 6
University of California, Irvine 6
Beihang University 6
Xiamen University 6
Microsoft Corporation 6
University of Chicago 6
University of California, San Diego 6
Pennsylvania State University 7
Nankai University 7
University of Science and Technology of China 7
Shanghai Jiaotong University 7
Argonne National Laboratory 7
EMC Corporation 7
University of Nebraska - Lincoln 8
Wayne State University 8
Florida State University 8
Princeton University 9
IBM Almaden Research Center 9
Intel Corporation 9
Florida International University 10
IBM Thomas J. Watson Research Center 10
Chinese University of Hong Kong 10
Texas A and M University 10
NetApp, USA 11
Auburn University 12
Wuhan National Laboratory for Optoelectronics 12
University of Illinois at Urbana-Champaign 12
Date Storage Institute, A-Star, Singapore 13
National Taiwan University 13
IBM Zurich Research Laboratory 14
Carnegie Mellon University 16
Hanyang University 17
Seoul National University 21
Microsoft Research 22
University of Toronto 22
Tsinghua University 23
University of California, Santa Cruz 25
Huazhong University of Science and Technology 29
Stony Brook University 43
University of Wisconsin Madison 51

ACM Transactions on Storage (TOS)
Archive


2017
Volume 13 Issue 4, November 2017  Issue-in-Progress
Volume 13 Issue 3, October 2017 Special Issue on FAST 2017 and Regular Papers
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

2016
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)

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

2014
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

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

2012
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

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

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

2009
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

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

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

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

2005
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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