| Title/Authors | Title | Research Artifacts
[?] A research
artifact is any by-product of a research project that is not
directly included in the published research paper. In Computer
Science research this is often source code and data sets, but
it could also be media, documentation, inputs to proof
assistants, shell-scripts to run experiments, etc.
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Application-specific quantum for multi-core platform scheduler Boris Teabe, Alain Tchana, Daniel Hagimont |
Application-specific quantum for multi-core platform scheduler Details |
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HAFT: hardware-assisted fault tolerance Dmitrii Kuvaiskii, Rasha Faqeh, Pramod Bhatotia, Pascal Felber, Christof Fetzer |
HAFT: hardware-assisted fault tolerance Details |
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Research produced artifacts
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Yoda: a highly available layer-7 load balancer Rohan Gandhi, Y. Charlie Hu, Ming Zhang |
Yoda: a highly available layer-7 load balancer Details |
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vScale: automatic and efficient processor scaling for SMP virtual machines Luwei Cheng, Jia Rao, Francis C. M. Lau |
vScale: automatic and efficient processor scaling for SMP virtual machines Details |
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Optimizing distributed actor systems for dynamic interactive services Andrew Newell, Gabriel Kliot, Ishai Menache, Aditya Gopalan, Soramichi Akiyama, Mark Silberstein |
Optimizing distributed actor systems for dynamic interactive services Details |
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Practical condition synchronization for transactional memory Chao Wang, Michael F. Spear |
Practical condition synchronization for transactional memory Details |
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The Linux scheduler: a decade of wasted cores Jean-Pierre Lozi, Baptiste Lepers, Justin R. Funston, Fabien Gaud, Vivien Quéma, Alexandra Fedorova |
The Linux scheduler: a decade of wasted cores Details |
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Partial-parallel-repair (PPR): a distributed technique for repairing erasure coded storage Subrata Mitra, Rajesh Krishna Panta, Moo-Ryong Ra, Saurabh Bagchi |
Partial-parallel-repair (PPR): a distributed technique for repairing erasure coded storage Details |
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TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters Alexey Tumanov, Timothy Zhu, Jun Woo Park, Michael A. Kozuch, Mor Harchol-Balter, Gregory R. Ganger |
TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters Details |
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Oasis: energy proportionality with hybrid server consolidation Junji Zhi, Nilton Bila, Eyal de Lara |
Oasis: energy proportionality with hybrid server consolidation Details |
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Picocenter: supporting long-lived, mostly-idle applications in cloud environments Liang Zhang, James Litton, Frank Cangialosi, Theophilus Benson, Dave Levin, Alan Mislove |
Picocenter: supporting long-lived, mostly-idle applications in cloud environments Details |
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Flint: batch-interactive data-intensive processing on transient servers Prateek Sharma, Tian Guo, Xin He, David E. Irwin, Prashant J. Shenoy |
Flint: batch-interactive data-intensive processing on transient servers Details |
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NChecker: saving mobile app developers from network disruptions Xinxin Jin, Peng Huang, Tianyin Xu, Yuanyuan Zhou |
NChecker: saving mobile app developers from network disruptions Details |
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POSIX abstractions in modern operating systems: the old, the new, and the missing Vaggelis Atlidakis, Jeremy Andrus, Roxana Geambasu, Dimitris Mitropoulos, Jason Nieh |
POSIX abstractions in modern operating systems: the old, the new, and the missing Details |
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GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing |
GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server Details |
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On the capacity of thermal covert channels in multicores Davide B. Bartolini, Philipp Miedl, Lothar Thiele |
On the capacity of thermal covert channels in multicores Details |
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Shared address translation revisited Xiaowan Dong, Sandhya Dwarkadas, Alan L. Cox |
Shared address translation revisited Details |
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TFC: token flow control in data center networks Jiao Zhang, Fengyuan Ren, Ran Shu, Peng Cheng |
TFC: token flow control in data center networks Details |
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Efficient queue management for cluster scheduling Jeff Rasley, Konstantinos Karanasos, Srikanth Kandula, Rodrigo Fonseca, Milan Vojnovic, Sriram Rao |
Efficient queue management for cluster scheduling Details |
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Sinter: low-bandwidth remote access for the visually-impaired Syed Masum Billah, Donald E. Porter, I. V. Ramakrishnan |
Sinter: low-bandwidth remote access for the visually-impaired Details |
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Parallel sections: scaling system-level data-structures Qi Wang, Timothy Stamler, Gabriel Parmer |
Parallel sections: scaling system-level data-structures Details |
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Crayon: saving power through shape and color approximation on next-generation displays Phillip Stanley-Marbell, Virginia Estellers, Martin C. Rinard |
Crayon: saving power through shape and color approximation on next-generation displays Details |
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Juggler: a practical reordering resilient network stack for datacenters Yilong Geng, Vimalkumar Jeyakumar, Abdul Kabbani, Mohammad Alizadeh |
Juggler: a practical reordering resilient network stack for datacenters Details |
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Increasing large-scale data center capacity by statistical power control Guosai Wang, Shuhao Wang, Bing Luo, Weisong Shi, Yinghang Zhu, Wenjun Yang, Dianming Hu, Longbo Huang, Xin Jin, Wei Xu |
Increasing large-scale data center capacity by statistical power control Details |
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A high performance file system for non-volatile main memory Jiaxin Ou, Jiwu Shu, Youyou Lu |
A high performance file system for non-volatile main memory Details |
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Type-aware transactions for faster concurrent code Nathaniel Herman, Jeevana Priya Inala, Yihe Huang, Lillian Tsai, Eddie Kohler, Barbara Liskov, Liuba Shrira |
Type-aware transactions for faster concurrent code Details |
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BB: booting booster for consumer electronics with modern OS Geunsik Lim, MyungJoo Ham |
BB: booting booster for consumer electronics with modern OS Details |
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Hardware read-write lock elision Pascal Felber, Shady Issa, Alexander Matveev, Paolo Romano |
Hardware read-write lock elision Details |
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PSLO: enforcing the Xth percentile latency and throughput SLOs for consolidated VM storage Ning Li, Hong Jiang, Dan Feng, Zhan Shi |
PSLO: enforcing the Xth percentile latency and throughput SLOs for consolidated VM storage Details |
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Fast and general distributed transactions using RDMA and HTM Yanzhe Chen, Xingda Wei, Jiaxin Shi, Rong Chen, Haibo Chen |
Fast and general distributed transactions using RDMA and HTM Details |
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pVM: persistent virtual memory for efficient capacity scaling and object storage Sudarsun Kannan, Ada Gavrilovska, Karsten Schwan |
pVM: persistent virtual memory for efficient capacity scaling and object storage Details |
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A study of modern Linux API usage and compatibility: what to support when you're supporting Chia-che Tsai, Bhushan Jain, Nafees Ahmed Abdul, Donald E. Porter |
A study of modern Linux API usage and compatibility: what to support when you're supporting Details |
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Data tiering in heterogeneous memory systems Subramanya Dulloor, Amitabha Roy, Zheguang Zhao, Narayanan Sundaram, Nadathur Satish, Rajesh Sankaran, Jeff Jackson, Karsten Schwan |
Data tiering in heterogeneous memory systems Details |
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zExpander: a key-value cache with both high performance and fewer misses Xingbo Wu, Li Zhang, Yandong Wang, Yufei Ren, Michel Hack, Song Jiang |
zExpander: a key-value cache with both high performance and fewer misses Details |
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Exploiting variability for energy optimization of parallel programs Wim Lavrijsen, Costin Iancu, Wibe de Jong, Xin Chen, Karsten Schwan |
Exploiting variability for energy optimization of parallel programs Details |
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Hold 'em or fold 'em?: aggregation queries under performance variations Gautam Kumar, Ganesh Ananthanarayanan, Sylvia Ratnasamy, Ion Stoica |
Hold 'em or fold 'em?: aggregation queries under performance variations Details |
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Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar |
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STRADS: a distributed framework for scheduled model parallel machine learning Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A. Gibson, Eric P. Xing |
STRADS: a distributed framework for scheduled model parallel machine learning Details |
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