We will collect and grow the reading list for the Storage Systems class (https://animeshtrivedi.github.io/course-stosys/) at VU Amsterdam. All lecture slides are publically available.
Hands-on wiki with commands and setup is available here: https://github.com/animeshtrivedi/storage-systems-wiki-reading-list/wiki
We are also collecting nice surveys we can find: https://github.com/stonet-research/storage-systems-wiki-reading-list#a-selection-of-storage-related-surveys
Contributions: please open a pull request with 1-2 line description of the paper!
- Improving Flash Storage Performance by Caching Address Mapping Table in Host Memory, https://www.usenix.org/conference/hotstorage17/program/presentation/jeong
- Jinghan Sun, Shaobo Li, Yunxin Sun, Chao Sun, Dejan Vucinic, and Jian Huang. 2023. LeaFTL: A Learning-Based Flash Translation Layer for Solid-State Drives. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS 2023). Association for Computing Machinery, New York, NY, USA, 442–456. https://doi.org/10.1145/3575693.3575744
- Aayush Gupta, Youngjae Kim, and Bhuvan Urgaonkar. 2009. DFTL: a flash translation layer employing demand-based selective caching of page-level address mappings. In Proceedings of the 14th international conference on Architectural support for programming languages and operating systems (ASPLOS XIV). Association for Computing Machinery, New York, NY, USA, 229–240. https://doi.org/10.1145/1508244.1508271
- S. Jiang, Lei Zhang, XinHao Yuan, Hao Hu and Yu Chen, "S-FTL: An efficient address translation for flash memory by exploiting spatial locality," 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies (MSST), Denver, CO, 2011, pp. 1-12, doi: 10.1109/MSST.2011.5937215.
- You Zhou, Fei Wu, Ping Huang, Xubin He, Changsheng Xie, and Jian Zhou. 2015. An efficient page-level FTL to optimize address translation in flash memory. In Proceedings of the Tenth European Conference on Computer Systems (EuroSys '15). Association for Computing Machinery, New York, NY, USA, Article 12, 1–16. https://doi.org/10.1145/2741948.2741949
- LearnedFTL: A Learning-Based Page-Level FTL for Reducing Double Reads in Flash-Based SSDs, https://doi.ieeecomputersociety.org/10.1109/HPCA57654.2024.00054
- Theano Stavrinos, Daniel S. Berger, Ethan Katz-Bassett, and Wyatt Lloyd. 2021. Don't be a blockhead: zoned namespaces make work on conventional SSDs obsolete. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '21). Association for Computing Machinery, New York, NY, USA, 144–151. https://doi.org/10.1145/3458336.3465300
- ZNS is the new and exciting interface for NVM storage. It is also quite fundamental as the paper argues that it solves or makes old problems with flash useless. A good fun read (plus, summarizes a decade worth of research effort in managing flash)
- FlatFlash: Exploiting the Byte-Accessibility of SSDs within a Unified Memory-Storage Hierarchy, ASPLOS 2019, https://dl-acm-org.vu-nl.idm.oclc.org/doi/abs/10.1145/3297858.3304061 *
- BlockFlex: Enabling Storage Harvesting with Software-Defined Flash in Modern Cloud Platforms, OSDI 2022. https://www.usenix.org/conference/osdi22/presentation/reidys
- FLIN: https://ieeexplore.ieee.org/abstract/document/8416843
- D2FQ: https://www.usenix.org/system/files/fast21-woo.pdf
- blk-switch: https://www.usenix.org/system/files/osdi21-hwang.pdf
- I/O Schedulers for Proportionality and Stability on Flash-Based SSDs in Multi-Tenant Environments
- Towards Low-Latency I/O Services for Mixed Workloads Using Ultra-Low Latency SSDs (FastResponse): https://dl.acm.org/doi/pdf/10.1145/3524059.3532378
- Configuring and Coordinating End-to-end QoS for Emerging Storage Infrastructure. ACM Trans. Model. Perform. Eval. Comput. Syst. 9, 1, Article 4 (March 2024), 32 pages. https://doi.org/10.1145/3631606
- Enabling NVMe WRR support in Linux Block Layer,https://www.usenix.org/conference/hotstorage17/program/presentation/joshi
- BFQ, Multiqueue-Deadline, or Kyber? Performance Characterization of Linux Storage Schedulers in the NVMe Era, https://dl.acm.org/doi/10.1145/3629526.3645053
- Do we still need IO schedulers for low-latency disks? https://dl.acm.org/doi/10.1145/3599691.3603400
- Liuying Ma, Zhenqing Liu, Jin Xiong, Yue Wu, Renhai Chen, Xi Peng, Ying Zhang, Gong Zhang, Dejun Jiang. 2024. zQoS: Unleashing full performance capabilities of NVMe SSDs while enforcing SLOs in distributed storage systems. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/abs/10.1145/3673038.3673156
- Shucheng Wang, Kaiye Zhou, Zhandong Guo, Qiang Cao, Jun Xu, Jie Yao. 2024. SIndex: An SSD-based Large-scale Indexing with Deterministic Latency for Cloud Block Storage. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/10.1145/3673038.3673041
- Kan Zhong, Zhiwang Yu, Qiao Li, Xianqiang Luo, Linbo Long, Yujian Tan, Ao Ren, Duo Liu. 2024. DPC: DPU-accelerated High-Performance File System Client. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/10.1145/3673038.3673123
- Stratos Idreos and Mark Callaghan. 2020. Key-Value Storage Engines. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD '20). Association for Computing Machinery, New York, NY, USA, 2667–2672. https://doi.org/10.1145/3318464.3383133
- A small write up summarizing (2020) the current popular indexing structures about KV data structures
- Designing Access Methods: The RUM Conjecture (Read, Update, and Memory): https://stratos.seas.harvard.edu/files/stratos/files/rum.pdf
- A good fundamental work that discusses that a single data structure cannot provide most optimal performance for all three types of operations
- https://www.vldb.org/pvldb/vol14/p2216-sarkar.pdf, Constructing and Analyzing the LSM Compaction Design Space
- DComp: Efficient Offload of LSM-tree Compaction with Data Processing Units, https://dl.acm.org/doi/abs/10.1145/3605573.3605633
- Ruisong Zhou, Yuzhan Zhang, Chunhua Li, Ke Zhou, Peng Wang, Gong Zhang, Ji Zhang, Guangyu Zhang. 2024. HyperDB: a Novel Key Value Store for Reducing Background Traffic in Heterogeneous SSD Storage. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/10.1145/3673038.3673153
- MDev-NVMe (mediated passthrough): https://www.usenix.org/system/files/conference/atc18/atc18-peng.pdf
- Analyzing, modeling, and provisioning QoS for NVMe ssds: https://ieeexplore.ieee.org/abstract/document/8603171/
- Spdk vhost-nvme (user-space): Accelerating i/os in virtual machines on nvme ssds via user space vhost target: https://ieeexplore.ieee.org/abstract/document/8567374/
- FlashBlox: https://www.usenix.org/system/files/conference/fast17/fast17_huang.pdf
- MQsim (simulator): https://www.usenix.org/system/files/conference/fast18/fast18-tavakkol.pdf
- FEMU (emulator): https://www.usenix.org/system/files/conference/fast18/fast18-li.pdf
- NVMeVirt (emulator): https://www.usenix.org/conference/fast23/presentation/kim-sang-hoon
- Amber https://ieeexplore.ieee.org/abstract/document/8574562 and for ZNS: https://ieeexplore.ieee.org/abstract/document/996440
- Wear distributions in FF-SSD: https://dl.acm.org/doi/abs/10.1145/3538643.3539757
- Nameless writes had an emulator. How does it work? https://www.usenix.org/legacy/event/fast12/tech/full_papers/Zhang2-7-12.pdf
- Goliath allows evaluating large (1TB at that time) workloads on small disks without performance gaps.
- DeepStore: In-Storage Acceleration for Intelligent Queries. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '52). Association for Computing Machinery, New York, NY, USA, 224–238. https://doi.org/10.1145/3352460.3358320
- Alberto Lerner and Philippe Bonnet. 2021. Not your Grandpa's SSD: The Era of Co-Designed Storage Devices. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21). Association for Computing Machinery, New York, NY, USA, 2852–2858. https://doi.org/10.1145/3448016.3457540
- DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines, https://pure.itu.dk/ws/files/86467917/CIDR2022.pdf
- Computational Storage: Where Are We Today? https://www.cidrdb.org/cidr2021/papers/cidr2021_paper29.pdf
- Cognitive SSD: A Deep Learning Engine for In-Storage Data Retrieval, https://www.usenix.org/conference/atc19/presentation/liang
- POLARDB Meets Computational Storage: Efficiently Support Analytical Workloads in Cloud-Native Relational Database, https://www.usenix.org/conference/fast20/presentation/cao-wei
- A Fast and Flexible Hardware-based Virtualization Mechanism for Computational Storage Devices, https://www.usenix.org/conference/atc21/presentation/kwon
- Leveraging Computational Storage for Power-Efficient Distributed Data Analytics. ACM Trans. Embed. Comput. Syst. 21, 6, Article 82 (November 2022), 36 pages. https://doi.org/10.1145/3528577
- Cost-effective, Energy-efficient, and Scalable Storage Computing for Large-scale AI Applications. ACM Trans. Storage 16, 4, Article 21 (November 2020), 37 pages. https://doi.org/10.1145/3415580
- SmartSAGE: training large-scale graph neural networks using in-storage processing architectures. In Proceedings of the 49th Annual International Symposium on Computer Architecture (ISCA '22). Association for Computing Machinery, New York, NY, USA, 932–945. https://doi.org/10.1145/3470496.3527391
- An Analytical Model-based Capacity Planning Approach for Building CSD-based Storage Systems. ACM Trans. Embed. Comput. Syst. Just Accepted (September 2023). https://doi.org/10.1145/3623677
- Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs, https://www.usenix.org/conference/atc24/presentation/tian
- Collection of papers projects: https://github.com/dnasc/graph-processing
- Graph Generators: https://github.com/yuanqidu/awesome-graph-generation
- Search: https://github.com/search?q=awesome-graph&type=repositories
- Mosaic: Processing a Trillion-Edge Graph on a Single Machine. In Proceedings of the Twelfth European Conference on Computer Systems (EuroSys '17). Association for Computing Machinery, New York, NY, USA, 527–543. https://doi.org/10.1145/3064176.3064191
- Efficient Large Graph Processing with Chunk-Based Graph Representation Model, https://www.usenix.org/conference/atc24/presentation/wang-rui
- Tarikul Islam Papon, Taishan Chen, Shuo Zhang, and Manos Athanassoulis. 2024. CAVE: Concurrency-Aware Graph Processing on SSDs. Proc. ACM Manag. Data 2, 3, Article 125 (June 2024), 26 pages. https://doi.org/10.1145/3654928
- Kiran Kumar Matam, Gunjae Koo, Haipeng Zha, Hung-Wei Tseng, and Murali Annavaram. 2019. GraphSSD: graph semantics aware SSD. In Proceedings of the 46th International Symposium on Computer Architecture (ISCA '19). Association for Computing Machinery, New York, NY, USA, 116–128. https://doi.org/10.1145/3307650.3322275
- FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs, https://www.usenix.org/conference/fast15/technical-sessions/presentation/zheng (Github)
- Juno Kim and Steven Swanson. 2022. Blaze: fast graph processing on fast SSDs. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (SC '22). IEEE Press, Article 44, 1–15. (Github: https://github.com/NVSL/blaze )
- TriCache: A User-Transparent Block Cache Enabling High-Performance Out-of-Core Processing with In-Memory Programs, https://www.usenix.org/conference/osdi22/presentation/feng (has graph as one of the driving workloads)
- "A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing," in IEEE Transactions on Computers, vol. 71, no. 7, pp. 1612-1625, 1 July 2022, doi: 10.1109/TC.2021.3098976.
- "A Hybrid Update Strategy for I/O-Efficient Out-of-Core Graph Processing," in IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 8, pp. 1767-1782, 1 Aug. 2020, doi: 10.1109/TPDS.2020.2973143.
- LUMOS: Dependency-Driven Disk-based Graph Processing, https://www.usenix.org/conference/atc19/presentation/vora https://github.com/pdclab/lumos
- GraphWalker: An I/O-Efficient and Resource-Friendly Graph Analytic System for Fast and Scalable Random Walks, https://www.usenix.org/conference/atc20/presentation/wang-rui
- MBFGraph: An SSD-based External Graph System for Evolving Graphs. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 24, 1–13. https://doi.org/10.1145/3581784.3607070
- GraFboost: using accelerated flash storage for external graph analytics. In Proceedings of the 45th Annual International Symposium on Computer Architecture (ISCA '18). IEEE Press, 411–424. https://doi.org/10.1109/ISCA.2018.00042
- GLIST: Towards In-Storage Graph Learning, https://www.usenix.org/conference/atc21/presentation/li-cangyuan
- Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs, https://www.usenix.org/conference/fast22/presentation/kwon
- GraFboost: using accelerated flash storage for external graph analytics. In Proceedings of the 45th Annual International Symposium on Computer Architecture (ISCA '18). IEEE Press, 411–424. https://doi.org/10.1109/ISCA.2018.00042
- Load the edges you need: a generic I/O optimization for disk-based graph processing. In Proceedings of the 2016 USENIX Conference on Usenix Annual Technical Conference (USENIX ATC '16). USENIX Association, USA, 507–522.
- Lambda-IO: A Unified IO Stack for Computational Storage, https://www.usenix.org/system/files/fast23-yang-zhe.pdf
- https://people.csail.mit.edu/jshun/graph.shtml
- Graphene: Fine-Grained IO Management for Graph Computing, https://www.usenix.org/conference/fast17/technical-sessions/presentation/liu (Github:https://github.com/iHeartGraph/Graphene)
- Seraph: Towards Scalable and Efficient Fully-external Graph Computation via On-demand Processing, https://www.usenix.org/conference/fast24/presentation/yang-tsun-yu
- Parallelizing Sequential Graph Computations. In Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD '17). Association for Computing Machinery, New York, NY, USA, 495–510. https://doi.org/10.1145/3035918.3035942
- Everything you always wanted to know about multicore graph processing but were afraid to ask, https://www.usenix.org/conference/atc17/technical-sessions/presentation/malicevic (https://github.com/jmalicevic/EverythingGraph)
- I/O Passthru: Upstreaming a flexible and efficient I/O Path in Linux, https://www.usenix.org/system/files/fast24-joshi.pdf
- Diego Didona, Jonas Pfefferle, Nikolas Ioannou, Bernard Metzler, and Animesh Trivedi. 2022. Understanding modern storage APIs: a systematic study of libaio, SPDK, and io_uring. In Proceedings of the 15th ACM International Conference on Systems and Storage (SYSTOR '22). Association for Computing Machinery, New York, NY, USA, 120–127. https://doi.org/10.1145/3534056.3534945
- Gabriel Haas and Viktor Leis. 2023. What Modern NVMe Storage Can Do, and How to Exploit it: High-Performance I/O for High-Performance Storage Engines. Proc. VLDB Endow. 16, 9 (May 2023), 2090–2102. https://doi.org/10.14778/3598581.3598584
- Zebin Ren and Animesh Trivedi. 2023. Performance Characterization of Modern Storage Stacks: POSIX I/O, libaio, SPDK, and io_uring. In Proceedings of the 3rd Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems (CHEOPS '23). Association for Computing Machinery, New York, NY, USA, 35–45. https://doi.org/10.1145/3578353.3589545
- Peter Desnoyers, Ian Adams, Tyler Estro, Anshul Gandhi, Geoff Kuenning, Mike Mesnier, Carl Waldspurger, Avani Wildani, and Erez Zadok. 2023. Persistent Memory Research in the Post-Optane Era. In Proceedings of the 1st Workshop on Disruptive Memory Systems (DIMES '23). Association for Computing Machinery, New York, NY, USA, 23–30. https://doi.org/10.1145/3609308.3625268
- Hasan Al Maruf, Hao Wang, Abhishek Dhanotia, Johannes Weiner, Niket Agarwal, Pallab Bhattacharya, Chris Petersen, Mosharaf Chowdhury, Shobhit Kanaujia, and Prakash Chauhan. 2023. TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 (ASPLOS 2023). Association for Computing Machinery, New York, NY, USA, 742–755. https://doi.org/10.1145/3582016.3582063
- Huaicheng Li, Daniel S. Berger, Lisa Hsu, Daniel Ernst, Pantea Zardoshti, Stanko Novakovic, Monish Shah, Samir Rajadnya, Scott Lee, Ishwar Agarwal, Mark D. Hill, Marcus Fontoura, and Ricardo Bianchini. 2023. Pond: CXL-Based Memory Pooling Systems for Cloud Platforms. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS 2023). Association for Computing Machinery, New York, NY, USA, 574–587. https://doi.org/10.1145/3575693.3578835
- Miryeong Kwon, Sangwon Lee, and Myoungsoo Jung. 2023. Cache in Hand: Expander-Driven CXL Prefetcher for Next Generation CXL-SSD. In Proceedings of the 15th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage '23). Association for Computing Machinery, New York, NY, USA, 24–30. https://doi.org/10.1145/3599691.3603406
- Myoungsoo Jung. 2022. Hello bytes, bye blocks: PCIe storage meets compute express link for memory expansion (CXL-SSD). In Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage '22). Association for Computing Machinery, New York, NY, USA, 45–51. https://doi.org/10.1145/3538643.3539745
- Jianguo Wang and Qizhen Zhang. 2023. Disaggregated Database Systems. In Companion of the 2023 International Conference on Management of Data (SIGMOD '23). Association for Computing Machinery, New York, NY, USA, 37–44. https://doi.org/10.1145/3555041.3589403
- Hasan Al Maruf and Mosharaf Chowdhury. 2023. Memory Disaggregation: Advances and Open Challenges. SIGOPS Oper. Syst. Rev. 57, 1 (June 2023), 29–37. https://doi.org/10.1145/3606557.3606562
- Marcos K. Aguilera, Emmanuel Amaro, Nadav Amit, Erika Hunhoff, Anil Yelam, and Gerd Zellweger. 2023. Memory disaggregation: why now and what are the challenges. SIGOPS Oper. Syst. Rev. 57, 1 (June 2023), 38–46. https://doi.org/10.1145/3606557.3606563
- Direct Access, High-Performance Memory Disaggregation with DirectCXL, https://www.usenix.org/conference/atc22/presentation/gouk
- FlatFS: Flatten Hierarchical File System Namespace on Non-volatile Memories, https://www.usenix.org/conference/atc22/presentation/cai
- Poseidon: Safe, Fast and Scalable Persistent Memory Allocator (Middleware, 2020), https://dl.acm.org/doi/10.1145/3423211.3425671
- Persistent State Machines for Recoverable In-memory Storage Systems with NVRam, https://www.usenix.org/conference/osdi20/presentation/zhang-wen
- Wenda Tang, Ying Han, Tianxiang Ai, Guanghui Li, Bin Yu, Xin Yang. 2024. Yggdrasil: Reducing Network I/O Tax with (CXL-Based) Distributed Shared Memory. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/10.1145/3673038.3673138
- RackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design, https://dl.acm.org/doi/10.1145/3600006.3613170
- Impact of Commodity Networks on Storage Disaggregation with NVMe-oF (2021) https://par.nsf.gov/servlets/purl/10299229
- Arjun Kashyap and Xiaoyi Lu. 2022. NVMe-oAF: Towards Adaptive NVMe-oF for IO-Intensive Workloads on HPC Cloud. In Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing (HPDC '22). Association for Computing Machinery, New York, NY, USA, 56–70. https://doi.org/10.1145/3502181.3531476
- Zvika Guz, Harry (Huan) Li, Anahita Shayesteh, and Vijay Balakrishnan. 2018. Performance Characterization of NVMe-over-Fabrics Storage Disaggregation. ACM Trans. Storage 14, 4, Article 31 (November 2018), 18 pages. https://doi.org/10.1145/3239563
- DDS: DPU-optimized Disaggregated Storage, https://arxiv.org/abs/2407.13618
- Reducing Minor Page Fault Overheads through Enhanced Page Walker, https://dl.acm.org/doi/full/10.1145/3547142
- BlockFlex: Enabling Storage Harvesting with Software-Defined Flash in Modern Cloud Platforms, https://my-henrys.github.io/papers/blockflex-osdi2022.pdf
- Sidharth Sundar, William Simpson, Jacob Higdon, Caeden Whitaker, Bryan Harris, and Nihat Altiparmak. 2023. Energy Implications of IO Interface Design Choices. In Proceedings of the 15th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage '23). Association for Computing Machinery, New York, NY, USA, 58–64. https://doi.org/10.1145/3599691.3603411
- Bryan Harris and Nihat Altiparmak. When poll is more energy efficient than interrupt. In Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems, HotStorage ’22, page 59–64, New York, NY, USA, 2022. Association for Computing Machinery. https://www.hotstorage.org/2022/slides/hotstorage22-paper44-presentation_slides.pdf
- Bryan Harris and Nihat Altiparmak. Ultra‐low latency ssds’ impact on overall energy efficiency. In Proceedings of the 12th USENIX Conference on Hot Topics in Storage and File Systems, HotStorage’20, USA, 2020. USENIX Association. https://www.usenix.org/system/files/hotstorage20-paper61-slides-harris.pdf
- Jing Li, Anirudh Badam, Ranveer Chandra, Steven Swanson, Bruce Worthington, and Qi Zhang. On the energy overhead of mobile storage systems. In Proceedings of the 12th USENIX Conference on File and Storage Technologies, FAST’14, page 105–118, USA, 2014. USENIX Association.
- Jayashree Mohan, Dhathri Purohith, Mathew Halpern, Vijay Chidambaram, and Vijay Janapa Reddi. Storage on your smartphone uses more energy than you think. In Proceedings of the 9th USENIX Conference on Hot Topics in Storage and File Systems, HotStorage’17, page 9, USA, 2017. USENIX Association.
- Kargar, Saeed and Nawab, Faisal. Hamming Tree: The Case for Energy-Aware Indexing for NVMs. In Proceedings of the ACM on Management of Data. SIGMOD'2023. ACM New York, NY, USA, 2023.
- Jalal Mostafa, Sara Wehbi, Suren Chilingaryan, Andreas Kopmann. 2022. SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things. https://arxiv.org/abs/2204.09795v2
- Paper describing a system for benchmarking the performance of timeseries databases. The paper generates data used to benchmark the performance of insertion and uses a few queries to test the performance of the querying engines.
- FpgaNIC: An FPGA-based Versatile 100Gb SmartNIC for GPUs https://www.usenix.org/system/files/atc22-wang-zeke.pdf
- Gabin Schieffer, Jacob Wahlgren, Jie Ren, Jennifer Faj, Ivy Peng. 2024. Harnessing Integrated CPU-GPU System Memory for HPC: a first look into Grace Hopper. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/10.1145/3673038.3673110
- Bjørling, M., Aghayev, A., Holmberg, H., Ramesh, A., Le Moal, D., Ganger, G. R., & Amvrosiadis, G. (2021). ZNS: Avoiding the Block Interface Tax for Flash-based SSDs. In 2021 USENIX Annual Technical Conference (USENIX ATC 21) (pp. 689-703). https://www.usenix.org/conference/atc21/presentation/bjorling . An explanation of ZNS and what it is good for.
- Bjørling, M. (2019, February). From open-channel SSDs to zoned namespaces. In Proc. Linux Storage Filesyst. Conf.(Vault) (Vol. 1). https://www.usenix.org/sites/default/files/conference/protected-files/nsdi19_slides_bjorling.pdf The transition from open-channel to ZNS.
- Theano Stavrinos, Daniel S. Berger, Ethan Katz-Bassett, and Wyatt Lloyd. 2021. Don't be a blockhead: zoned namespaces make work on conventional SSDs obsolete. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '21). Association for Computing Machinery, New York, NY, USA, 144–151. https://doi.org/10.1145/3458336.3465300
- Append is Near: Log-based Data Management on ZNS SSDs. https://www.ssrc.ucsc.edu/media/pubs/8698b15f3152427d1285a995af615fbe7be26c7b.pdf Explanation of ZNS append operation, introduction of Group Append, advantages of alternatives, and various research directions for file systems, LSM-trees, databases and logs.
- Han, K., Gwak, H., Shin, D., & Hwang, J. (2021). ZNS+: Advanced zoned namespace interface for supporting in-storage zone compaction. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) (pp. 147-162). https://www.usenix.org/conference/osdi21/presentation/han A new LFS-aware ZNS interface, where the host can offload data copy operations to the SSD, and more functionalities that aids such file systems (threaded logging support).
- Maheshwari, U. (2021, July). From blocks to rocks: A natural extension of zoned namespaces. In Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems (pp. 21-27). https://dl.acm.org/doi/abs/10.1145/3465332.3470870 Extend ZNS with "rocks", support for variable-size pieces of data.
- Tehrany, N., & Trivedi, A. (2022). Understanding NVMe Zoned Namespace (ZNS) Flash SSD Storage Devices. arXiv preprint arXiv:2206.01547. https://arxiv.org/abs/2206.01547 A systemic analysis of integration options, current software support and initial performance measurements for ZNS.
- Bae, H., Kim, J., Kwon, M., & Jung, M. (2022, June). What you can't forget: exploiting parallelism for zoned namespaces. In Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems (pp. 79-85). https://dl.acm.org/doi/abs/10.1145/3538643.3539744 Discussion of ZNS benefits and how ZNS zone-to-zone relationships affect internal parallelism/performance, also how schedulers can solve such isssues.
- Zoned Namespaces Use Cases, Standard and Linux Ecosystem. Samsung, 2020 SNIA. https://www.snia.org/sites/default/files/SDCEMEA/2020/3%20-%20Javier%20Gonzalez%20Zoned%20namespacese.PDF
- Renping Liu, Junhua Chen, Peng Chen, Linbo Long, Anping Xiong, Duo Liu. 2024. Hi-ZNS: High Space Efficiency and Zero-Copy LSM-tree-based Stores on ZNS SSDs. In Proceedings of the 53rd International Conference on Parallel Processing. https://dl.acm.org/doi/10.1145/3673038.3673096
- Choi, G., Lee, K., Oh, M., Choi, J., Jhin, J., & Oh, Y. (2020). A New LSM-style Garbage Collection Scheme for ZNS SSDs. In 12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 20). https://www.usenix.org/conference/hotstorage20/presentation/choi
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[2023]
- A Survey on the Integration of NAND Flash Storage in the Design of File Systems and the Host Storage Software Stack, Nick Tehrany, Krijn Doekemeijer, Animesh Trivedi, https://arxiv.org/abs/2307.11866
[2022]
- Krijn Doekemeijer, Animesh Trivedi - Key-Value Stores on Flash Storage Devices: A Survey, https://arxiv.org/abs/2205.07975, May 2022.
- Persistent Memory File Systems: A Survey, Wiebe van Breukelen, https://drive.google.com/file/d/1EF-tTEDwYYoFOzywlC-STLqHYlGmyGDx/view
- http://madsys.cs.tsinghua.edu.cn/publications/TPDS2022-ma.pdf, a Survey of Storage Systems in the RDMA Era.
- Survey of Distributed File System Design Choices, https://dl.acm.org/doi/pdf/10.1145/3465405
[prior]
- Athanasios Fevgas, Leonidas Akritidis, Panayiotis Bozanis, and Yannis Manolopoulos. 2020. Indexing in flash storage devices: a survey on challenges, current approaches, and future trends. The VLDB Journal 29, 1 (Jan 2020), 273–311. https://doi.org/10.1007/s00778-019-00559-8 https://link.springer.com/content/pdf/10.1007/s00778-019-00559-8.pdf
- One of the most comprehensive and well-written surveys on indexing data structures on flash storage