research-article

Realizing Strong Determinism Contract on Log-Structured Merge Key-Value Stores

Authors:

Jooyoung Hwang,

Myoungsoo JungAuthors Info & Claims

ACM Transactions on Storage, Volume 19, Issue 2

Article No.: 11, Pages 1 - 29

https://doi.org/10.1145/3582695

Published: 25 March 2023 Publication History

Abstract

We propose Vigil-KV, a hardware and software co-designed framework that eliminates long-tail latency almost perfectly by introducing strong latency determinism. To make Get latency deterministic, Vigil-KV first enables a predictable latency mode (PLM) interface on a real datacenter-scale NVMe SSD, having knowledge about the nature of the underlying flash technologies. Vigil-KV at the system-level then hides the non-deterministic time window (associated with SSD’s internal tasks and/or write services) by internally scheduling the different device states of PLM across multiple physical functions. Vigil-KV further schedules compaction/flush operations and client requests being aware of PLM’s restrictions thereby integrating strong latency determinism into LSM KVs. We implement Vigil-KV upon a 1.92TB NVMe SSD prototype and Linux 4.19.91, but other LSM KVs can adopt its concept. We evaluate diverse Facebook and Yahoo scenarios with Vigil-KV, and the results show that Vigil-KV can reducethe tail latency of a baseline KV system by 3.19× while reducing the average latency by 34%, on average.

References

[1]

Nitin Agrawal, Vijayan Prabhakaran, Ted Wobber, John D. Davis, Mark S. Manasse, and Rina Panigrahy. 2008. Design tradeoffs for SSD performance. In Proceedings of the USENIX Annual Technical Conference.

[2]

Alaa R. Alameldeen, Ilya Wagner, Zeshan Chishti, Wei Wu, Chris Wilkerson, and Shih-Lien Lu. 2011. Energy-efficient cache design using variable-strength error-correcting codes. ACM SIGARCH Computer Architecture News 39, 3 (2011), 461–472.

Digital Library

[3]

Amazon. (n.d.). Amazon Found Every 100ms of Latency Cost them 1ry-100ms-of-latency-cost-them-1-in-sales.

[4]

Michael Anderson, Benny Chen, Stephen Chen, Summer Deng, Jordan Fix, Michael Gschwind, Aravind Kalaiah, Changkyu Kim, Jaewon Lee, Jason Liang, Haixin Liu, Yinghai Lu, Jack Montgomery, Arun Moorthy, Satish Nadathur, Sam Naghshineh, Avinash Nayak, Jongsoo Park, Chris Petersen, Martin Schatz, Narayanan Sundaram, Bangsheng Tang, Peter Tang, Amy Yang, Jiecao Yu, Hector Yuen, Ying Zhang, Aravind Anbudurai, Vandana Balan, Harsha Bojja, Joe Boyd, Matthew Breitbach, Claudio Caldato, Anna Calvo, Garret Catron, Sneh Chandwani, Panos Christeas, Brad Cottel, Brian Coutinho, Arun Dalli, Abhishek Dhanotia, Oniel Duncan, Roman Dzhabarov, Simon Elmir, Chunli Fu, Wenyin Fu, Michael Fulthorp, Adi Gangidi, Nick Gibson, Sean Gordon, Beatriz Padilla Hernandez, Daniel Ho, Yu-Cheng Huang, Olof Johansson, Shishir Juluri, Shobhit Kanaujia, Manali Kesarkar, Jonathan Killinger, Ben Kim, Rohan Kulkarni, Meghan Lele, Huayu Li, Huamin Li, Yueming Li, Cynthia Liu, Jerry Liu, Bert Maher, Chandra Mallipedi, Seema Mangla, Kiran Kumar Matam, Jubin Mehta, Shobhit Mehta, Christopher Mitchell, Bharath Muthiah, Nitin Nagarkatte, Ashwin Narasimha, Bernard Nguyen, Thiara Ortiz, Soumya Padmanabha, Deng Pan, Ashwin Poojary, Ye (Charlotte)Qi, Olivier Raginel, Dwarak Rajagopal, Tristan Rice, Craig Ross, Nadav Rotem, Scott Russ, Kushal Shah, Baohua Shan, Hao Shen, Pavan Shetty, Krish Skandakumaran, Kutta Srinivasan, Roshan Sumbaly, Michael Tauberg, Mor Tzur, Sidharth Verma, Hao Wang, Man Wang, Ben Wei, Alex Xia, Chenyu Xu, Martin Yang, Kai Zhang, Ruoxi Zhang, Ming Zhao, Whitney Zhao, Rui Zhu, Ajit Mathews, Lin Qiao, Misha Smelyanskiy, Bill Jia, and Vijay Rao. 2021. First-Generation Inference Accelerator Deployment at Facebook. arXiv preprint arXiv:2107.04140 (2021).

[5]

Timothy G. Armstrong, Vamsi Ponnekanti, Dhruba Borthakur, and Mark Callaghan. 2013. LinkBench: A database benchmark based on the Facebook social graph. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data.

Digital Library

[6]

Mahesh Balakrishnan, Asim Kadav, Vijayan Prabhakaran, and Dahlia Malkhi. 2010. Differential raid: Rethinking raid for ssd reliability. ACM Transactions on Storage 6, 2 (2010), 1–22.

Digital Library

[7]

Oana Balmau, Diego Didona, Rachid Guerraoui, Willy Zwaenepoel, Huapeng Yuan, Aashray Arora, Karan Gupta, and Pavan Konka. 2017. TRIAD: Creating synergies between memory, disk and log in log structured key-value stores. In Proceedings of the 2017 USENIX Annual Technical Conference.

[8]

Oana Balmau, Florin Dinu, Willy Zwaenepoel, Karan Gupta, Ravishankar Chandhiramoorthi, and Diego Didona. 2019. SILK: Preventing latency spikes in log-structured merge key-value stores. In Proceedings of the 2019 USENIX Annual Technical Conference.

[9]

Yu Cai, Saugata Ghose, Erich F. Haratsch, Yixin Luo, and Onur Mutlu. 2017. Error characterization, mitigation, and recovery in flash-memory-based solid-state drives. Proceedings of the IEEE 105, 9 (2017), 1666–1704.

[10]

Yu Cai, Saugata Ghose, Erich F. Haratsch, Yixin Luo, and Onur Mutlu. 2017. Error characterization, mitigation, and recovery in flash-memory-based solid-state drives. Proc. IEEE 105, 9 (2017), 1666–1704.

[11]

Yu Cai, Saugata Ghose, Yixin Luo, Ken Mai, Onur Mutlu, and Erich F. Haratsch. 2017. Vulnerabilities in MLC NAND flash memory programming: Experimental analysis, exploits, and mitigation techniques. In Proceedings of the 2017 IEEE International Symposium on High Performance Computer Architecture. IEEE.

[12]

Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai. 2012. Error patterns in MLC NAND flash memory: Measurement, characterization, and analysis. In Proceedings of the 2012 Design, Automation & Test in Europe Conference & Exhibition. IEEE.

[13]

Yu Cai, Erich F. Haratsch, Onur Mutlu, and Ken Mai. 2013. Threshold voltage distribution in MLC NAND flash memory: Characterization, analysis, and modeling. In Proceedings of the 2013 Design, Automation & Test in Europe Conference & Exhibition. IEEE.

[14]

Yu Cai, Yixin Luo, Saugata Ghose, and Onur Mutlu. 2015. Read disturb errors in MLC NAND flash memory: Characterization, mitigation, and recovery. In Proceedings of the 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. IEEE.

Digital Library

[15]

Yu Cai, Onur Mutlu, Erich F. Haratsch, and Ken Mai. 2013. Program interference in MLC NAND flash memory: Characterization, modeling, and mitigation. In Proceedings of the 2013 IEEE 31st International Conference on Computer Design. IEEE.

[16]

Zhichao Cao, Siying Dong, Sagar Vemuri, and David HC Du. 2020. Characterizing, modeling, and benchmarking rocksdb key-value workloads at facebook. In Proceedings of the 18th USENIX Conference on File and Storage Technologies.

Digital Library

[17]

Li-Pin Chang, Tei-Wei Kuo, and Shi-Wu Lo. 2004. Real-time garbage collection for flash-memory storage systems of real-time embedded systems. ACM Transactions on Embedded Computing Systems 3, 4 (2004), 837–863.

Digital Library

[18]

Hao Chen, Chaoyi Ruan, Cheng Li, Xiaosong Ma, and Yinlong Xu. 2021. SpanDB: A fast, cost-effective LSM-tree based KV store on hybrid storage. In Proceedings of the 19th USENIX Conference on File and Storage Technologies.

[19]

Wonil Choi, Myoungsoo Jung, Mahmut Kandemir, and Chita Das. 2018. Parallelizing garbage collection with I/O to improve flash resource utilization. In Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing.

Digital Library

[20]

Alexander Conway, Abhishek Gupta, Vijay Chidambaram, Martin Farach-Colton, Richard Spillane, Amy Tai, and Rob Johnson. 2020. SplinterDB: Closing the bandwidth gap for nvme key-value stores. In Proceedings of the 2020 USENIX Annual Technical Conference.

[21]

Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. 2010. Benchmarking cloud serving systems with YCSB. In Proceedings of the 1st ACM Symposium on Cloud Computing.

Digital Library

[22]

Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J. Franklin, Joseph E. Gonzalez, and Ion Stoica. 2017. Clipper: A \(\lbrace\)Low-Latency\(\rbrace\) online prediction serving system. In Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation. 613–627.

[23]

Niv Dayan, Manos Athanassoulis, and Stratos Idreos. 2017. Monkey: Optimal navigable key-value store. In Proceedings of the 2017 ACM International Conference on Management of Data.

Digital Library

[24]

Niv Dayan and Stratos Idreos. 2018. Dostoevsky: Better space-time trade-offs for LSM-tree based key-value stores via adaptive removal of superfluous merging. In Proceedings of the 2018 International Conference on Management of Data.

Digital Library

[25]

Siying Dong, Mark Callaghan, Leonidas Galanis, Dhruba Borthakur, Tony Savor, and Michael Strum. 2017. Optimizing space amplification in RocksDB. In Proceedings of the CIDR.

[26]

Siying Dong, Andrew Kryczka, Yanqin Jin, and Michael Stumm. 2021. Evolution of development priorities in key-value stores serving large-scale applications: The RocksDB experience. In Proceedings of the 19th USENIX Conference on File and Storage Technologies.

Digital Library

[27]

Siying Dong, Andrew Kryczka, Yanqin Jin, and Michael Stumm. 2021. RocksDB: Evolution of development priorities in a key-value store serving large-scale applications. ACM Transactions on Storage 17, 4 (2021), 1–32.

Digital Library

[28]

Assaf Eisenman, Darryl Gardner, Islam AbdelRahman, Jens Axboe, Siying Dong, Kim Hazelwood, Chris Petersen, Asaf Cidon, and Sachin Katti. 2018. Reducing DRAM footprint with NVM in Facebook. In Proceedings of the 13th EuroSys Conference.

Digital Library

[29]

Facebook. (n.d.). RocksDB: A Persistent Key-value Store for Fast Storage Environments. Retrieved from https://rocksdb.org

[30]

Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, and Christina Delimitrou. 2019. Seer: Leveraging big data to navigate the complexity of performance debugging in cloud microservices. In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems. 19–33.

Digital Library

[31]

Sanjay Ghemawat and Jeff Dean. (n.d.). LevelDB. Retrieved from https://github.com/google/leveldb

[32]

Google. (n.d.). Benchmarks for Mobile Page Speed. Retrieved from https://www.thinkwithgoogle.com/intl/en-ca/marketing-strategies/app-and-mobile/mobile-page-speed-new-industry-benchmarks

[33]

TABB Group. (n.d.). The Value of a Millisecond: Finding the Optimal Speed of a Trading Infrastructure. Retrieved from https://research.tabbgroup.com/report/v06-007-value-millisecond-finding-optimal-speed-trading-infrastructure

[34]

gRPC. (n.d.). gRPC: A high performance open-source universal RPC framework.Retrieved from https://grpc.io

[35]

Aayush Gupta, Youngjae Kim, and Bhuvan Urgaonkar. 2009. DFTL: A flash translation layer employing demand-based selective caching of page-level address mappings. Acm Sigplan Notices 44, 3 (2009), 229–240.

Digital Library

[36]

Keonsoo Ha, Jaeyong Jeong, and Jihong Kim. 2015. An integrated approach for managing read disturbs in high-density NAND flash memory. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35, 7 (2015), 1079–1091.

[37]

Ping Huang, Pradeep Subedi, Xubin He, Shuang He, and Ke Zhou. 2014. FlexECC: Partially relaxing ECC of MLCSSD for better cache performance. In Proceedings of the 2014 USENIX Annual Technical Conference (USENIXATC’14).

[38]

Junsu Im, Jinwook Bae, Chanwoo Chung, Sungjin Lee, Arvind Arvind. 2020. PinK: High-speed in-storage key-value store with bounded tails. In Proceedings of the 2020 USENIX Annual Technical Conference (USENIXATC’20).

[39]

Instagram. (n.d.). Performance & Usage at Instagram. Retrieved from https://instagram-engineering.com/performance-usage-at-instagram-d2ba0347e442

[40]

Tianyang Jiang, Guangyan Zhang, Zican Huang, Xiaosong Ma, Junyu Wei, Zhiyue Li, and Weimin Zheng. 2021. FusionRAID: Achieving consistent low latency for commodity SSD arrays. In Proceedings of the 19th USENIX Conference on File and Storage Technologies (FAST’21).

[41]

Xavier Jimenez, David Novo, and Paolo Ienne. 2014. Wear unleveling: Improving NAND flash lifetime by balancing page endurance. In Proceedings of the 12th USENIX Conference on File and Storage Technologies.

[42]

Heeseung Jo, Jeong-Uk Kang, Seon-Yeong Park, Jin-Soo Kim, and Joonwon Lee. 2006. FAB: Flash-aware buffer management policy for portable media players. IEEE Transactions on Consumer Electronics 52, 2 (2006), 485–493.

[43]

Myoungsoo Jung, Wonil Choi, Miryeong Kwon, Shekhar Srikantaiah, Joonhyuk Yoo, and Mahmut Taylan Kandemir. 2019. Design of a host interface logic for gc-free ssds. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, 8 (2019), 1674–1687.

[44]

Myoungsoo Jung, Wonil Choi, Shekhar Srikantaiah, Joonhyuk Yoo, and Mahmut T. Kandemir. 2014. HIOS: A host interface I/O scheduler for solid state disks. ACM SIGARCH Computer Architecture News 42, 3 (2014), 289–300.

Digital Library

[45]

M. Jung and M. Kandemir. 2013. Revisiting widely held SSD expectations and rethinking system-level implications. SIGMETRICS Perform. Eval. Rev. 41, 1 (June 2013), 203–216.

Digital Library

[46]

Myoungsoo Jung, Ramya Prabhakar, and Mahmut T. Kandemir. 2012. Taking garbage collection overheads off the critical path in SSDs. In Proceedings of the Middleware 2012 - ACM/IFIP/USENIX 13th International Middleware Conference, Montreal.Lecture Notes in Computer Science, Vol. 7662. Springer, 164–186.

[47]

Olzhas Kaiyrakhmet, Songyi Lee, Beomseok Nam, Sam H. Noh, and Young-ri Choi. 2019. SLM-DB: Single-level key-value store with persistent memory. In Proceedings of the17th USENIX Conference on File and Storage Technologies (FAST’19).

[48]

Jeong-Uk Kang, Heeseung Jo, Jin-Soo Kim, and Joonwon Lee. 2006. A superblock-based flash translation layer for NAND flash memory. In Proceedings of the 6th ACM & IEEE International Conference on Embedded Software.

Digital Library

[49]

Sooyong Kang, Sungmin Park, Hoyoung Jung, Hyoki Shim, and Jaehyuk Cha. 2008. Performance trade-offs in using NVRAM write buffer for flash memory-based storage devices. IEEE Transactions on Computers 58, 6 (2008), 744–758.

[50]

Woon-Hak Kang, Sang-Won Lee, Bongki Moon, Yang-Suk Kee, and Moonwook Oh. 2014. Durable write cache in flash memory SSD for relational and NoSQL databases. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data.

Digital Library

[51]

Sudarsun Kannan, Nitish Bhat, Ada Gavrilovska, Andrea Arpaci-Dusseau, and Remzi Arpaci-Dusseau. 2018. Redesigning LSMs for nonvolatile memory with NoveLSM. In Proceedings of the 2018 USENIX Annual Technical Conference.

[52]

Bryan S Kim, Jongmoo Choi, and Sang Lyul Min. 2019. Design tradeoffs for SSD reliability. In Proceedings of the 17th USENIX Conference on File and Storage Technologies.

Digital Library

[53]

Bryan S Kim, Hyun Suk Yang, and Sang Lyul Min. 2018. AutoSSD: An autonomic SSD architecture. In Proceedings of the 2018 USENIX Annual Technical Conference.

[54]

Hyojun Kim and Seongjun Ahn. 2008. BPLRU: A buffer management scheme for improving random writes in flash storage. In Proceedings of the File and Storage Technologies.

[55]

Jaeho Kim, Kwanghyun Lim, Youngdon Jung, Sungjin Lee, Changwoo Min, and Sam H. Noh. 2019. Alleviating garbage collection interference through spatial separation in all flash arrays. In Proceedings of the 2019 USENIX Annual Technical Conference.

Digital Library

[56]

Sungjoon Koh, Junhyeok Jang, Changrim Lee, Miryeong Kwon, Jie Zhang, and Myoungsoo Jung. 2019. Faster than flash: An in-depth study of system challenges for emerging ultra-low latency SSDs. In Proceedings of the IEEE International Symposium on Workload Characterization, IISWC 2019, Orlando, FL, USA, November 3-5, 2019. IEEE.

[57]

Sungjoon Koh, Changrim Lee, Miryeong Kwon, and Myoungsoo Jung. 2018. Exploring system challenges of ultra-low latency solid state drives. In Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems.

Digital Library

[58]

Sanjeev Kumar. (n.d.). Social networking at scale. Retrieved from https://www.ece.lsu.edu/hpca-18/files/HPCA2012_Facebook_Keynote.pdf.

[59]

Sangwon Lee, Miryeong Kwon, Gyuyoung Park, and Myoungsoo Jung. 2022. LightPC: Hardware and software co-design for energy-efficient full system persistence. In Proceedings of the 49th Annual International Symposium on Computer Architecture. 289–305.

Digital Library

[60]

Baptiste Lepers, Oana Balmau, Karan Gupta, and Willy Zwaenepoel. 2019. Kvell: The design and implementation of a fast persistent key-value store. In Proceedings of the 27th ACM Symposium on Operating Systems Principles.

Digital Library

[61]

Huaicheng Li, Martin L. Putra, Ronald Shi, Xing Lin, Gregory R. Ganger, and Haryadi S. Gunawi. 2021. lODA: A host/device co-design for strong predictability contract on modern flash storage. In Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles.

Digital Library

[62]

Qiao Li, Liang Shi, Chun Jason Xue, Kaijie Wu, Cheng Ji, Qingfeng Zhuge, and Edwin H-M Sha. 2016. Access characteristic guided read and write cost regulation for performance improvement on flash memory. In Proceedings of the 14th USENIX Conference on File and Storage Technologies.

Digital Library

[63]

Yongkun Li, Zhen Liu, Patrick PC Lee, Jiayu Wu, Yinlong Xu, Yi Wu, Liu Tang, Qi Liu, and Qiu Cui. 2021. Differentiated key-value storage management for balanced I/O performance. In Proceedings of the 2021 USENIX Annual Technical Conference.

[64]

Hyeontaek Lim, Bin Fan, David G. Andersen, and Michael Kaminsky. 2011. SILT: A memory-efficient, high-performance key-value store. In Proceedings of the 23rd ACM Symposium on Operating Systems Principles.

Digital Library

[65]

Chun-Yi Liu, Jagadish B. Kotra, Myoungsoo Jung, Mahmut T. Kandemir, and Chita R. Das. 2019. SOML read: Rethinking the read operation granularity of 3D NAND SSDs. In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems.

Digital Library

[66]

Lanyue Lu, Thanumalayan Sankaranarayana Pillai, Hariharan Gopalakrishnan, Andrea C. Arpaci-Dusseau, and Remzi H. Arpaci-Dusseau. 2017. Wisckey: Separating keys from values in ssd-conscious storage. ACM Transactions on Storage 13, 1 (2017), 1–28.

Digital Library

[67]

Bo Mao, Hong Jiang, Suzhen Wu, Lei Tian, Dan Feng, Jianxi Chen, and Lingfang Zeng. 2012. HPDA: A hybrid parity-based disk array for enhanced performance and reliability. ACM Transactions on Storage 8, 1 (2012), 1–20.

Digital Library

[68]

Yoshinori Matsunobu, Siying Dong, and Herman Lee. 2020. MyRocks: LSM-tree database storage engine serving Facebook’s social graph. Proceedings of the VLDB Endowment 13, 12 (2020), 3217–3230.

Digital Library

[69]

Neal Mielke, Todd Marquart, Ning Wu, Jeff Kessenich, Hanmant Belgal, Eric Schares, Falgun Trivedi, Evan Goodness, and Leland R Nevill. 2008. Bit error rate in NAND flash memories. In Proceedings of the 2008 IEEE International Reliability Physics Symposium. IEEE.

[70]

NVM Express, Inc. (n.d.). NVM Express Specification. Retrieved from https://nvmexpress.org/specifications

[71]

Gyuyoung Park, Miryeong Kwon, Pratyush Mahapatra, Michael Swift, and Myoungsoo Jung. 2018. BIBIM: A prototype multi-partition aware heterogeneous new memory. In Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage’18).

[72]

Stan Park and Kai Shen. 2012. FIOS: A fair, efficient flash I/O scheduler. In Proceedings of the File and Storage Technologies.

[73]

Pandian Raju, Rohan Kadekodi, Vijay Chidambaram, and Ittai Abraham. 2017. Pebblesdb: Building key-value stores using fragmented log-structured merge trees. In Proceedings of the 26th Symposium on Operating Systems Principles.

Digital Library

[74]

Kai Ren, Qing Zheng, Joy Arulraj, and Garth Gibson. 2017. SlimDB: A space-efficient key-value storage engine for semi-sorted data. Proceedings of the VLDB Endowment 10, 13 (2017), 2037–2048.

Digital Library

[75]

RPC. (n.d.). Apache Thrift. Retrieved from https://thrift.apache.org

[76]

Bianca Schroeder, Raghav Lagisetty, and Arif Merchant. 2016. Flash reliability in production: The expected and the unexpected. In Proceedings of the 14th USENIX Conference on File and Storage Technologies. 67–80. Retrieved from https://www.usenix.org/conference/fast16/technical-sessions/presentation/schroeder

Digital Library

[77]

Russell Sears and Raghu Ramakrishnan. 2012. bLSM: A general purpose log structured merge tree. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.

Digital Library

[78]

Narges Shahidi, Mahmut T. Kandemir, Mohammad Arjomand, Chita R. Das, Myoungsoo Jung, and Anand Sivasubramaniam. 2016. Exploring the potentials of parallel garbage collection in ssds for enterprise storage systems. In SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE.

Digital Library

[79]

Hongbin Sun, Wenzhe Zhao, Minjie Lv, Guiqiang Dong, Nanning Zheng, and Tong Zhang. 2016. Exploiting intracell bit-error characteristics to improve min-sum LDPC decoding for MLC NAND flash-based storage in mobile device. IEEE Transactions on Very Large Scale Integration Systems 24, 8 (2016), 2654–2664.

Digital Library

[80]

Arash Tavakkol, Mohammad Sadrosadati, Saugata Ghose, Jeremie Kim, Yixin Luo, Yaohua Wang, Nika Mansouri Ghiasi, Lois Orosa, Juan Gómez-Luna, and Onur Mutlu. 2018. FLIN: Enabling fairness and enhancing performance in modern NVMe solid state drives. In Proceedings of the2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture. IEEE.

Digital Library

[81]

Benny Van Houdt. 2013. A mean field model for a class of garbage collection algorithms in flash-based solid state drives. ACM SIGMETRICS Performance Evaluation Review 41, 1 (2013), 191–202.

Digital Library

[82]

Shucheng Wang, Ziyi Lu, Qiang Cao, Hong Jiang, Jie Yao, Yuanyuan Dong, and Puyuan Yang. 2020. BCW: Buffer-controlled writes to HDDs for SSD-HDD hybrid storage server. In Proceedings of the 18th USENIX Conference on File and Storage Technologies (FAST’20).

[83]

Wei Wang, Jinyang Gao, Meihui Zhang, Sheng Wang, Gang Chen, Teck Khim Ng, Beng Chin Ooi, Jie Shao, and Moaz Reyad. 2018. Rafiki: machine learning as an analytics service system. Proc. VLDB Endow. 12, 2 (2018), 128–140. https://arxiv.org/abs/1804.06087

[84]

Guanying Wu and Xubin He. 2012. Reducing SSD read latency via NAND flash program and erase suspension. In Proceedings of the File and Storage Technologies.

[85]

Shiqin Yan, Huaicheng Li, Mingzhe Hao, Michael Hao Tong, Swaminathan Sundararaman, Andrew A Chien, and Haryadi S Gunawi. 2017. Tiny-tail flash: Near-perfect elimination of garbage collection tail latencies in NAND SSDs. ACM Transactions on Storage 13, 3 (2017)1–26.

Digital Library

[86]

Ming-Chang Yang, Yu-Ming Chang, Che-Wei Tsao, Po-Chun Huang, Yuan-Hao Chang, and Tei-Wei Kuo. 2014. Garbage collection and wear leveling for flash memory: Past and future. In Proceedings of the 2014 International Conference on Smart Computing. IEEE.

[87]

Pan Yang, Ni Xue, Yuqi Zhang, Yangxu Zhou, Li Sun, Wenwen Chen, Zhonggang Chen, Wei Xia, Junke Li, and Kihyoun Kwon. 2019. Reducing garbage collection overhead in SSD based on workload prediction. In Proceedings of the 11th USENIX Workshop on Hot Topics in Storage and File Systems.

Digital Library

[88]

Jie Zhang, Miryeong Kwon, Donghyun Gouk, Sungjoon Koh, Nam Sung Kim, Mahmut Taylan Kandemir, and Myoungsoo Jung. 2021. Revamping storage class memory with hardware automated memory-over-storage solution. In Proceedings of the 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture. IEEE.

Digital Library

[89]

Jie Zhang, Miryeong Kwon, Donghyun Gouk, Sungjoon Koh, Changlim Lee, Mohammad Alian, Myoungjun Chun, Mahmut Taylan Kandemir, Nam Sung Kim, Jihong Kim, and Myoungsoo Jung. 2018. FlashShare: Punching through server storage stack from kernel to firmware for ultra-low latency SSDs. In Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, Andrea C. Arpaci-Dusseau and Geoff Voelker (Eds.). USENIX Association, 477–492.

[90]

Jie Zhang, Gyuyoung Park, David Donofrio, John Shalf, and Myoungsoo Jung. 2020. DRAM-less: Hardware acceleration of data processing with new memory. In Proceedings of the 2020 IEEE International Symposium on High Performance Computer Architecture. IEEE.

[91]

Meng Zhang, Fei Wu, Xubin He, Ping Huang, Shunzhuo Wang, and Changsheng Xie. 2016. REAL: A retention error aware LDPC decoding scheme to improve NAND flash read performance. In Proceedings of the 2016 32nd Symposium on Mass Storage Systems and Technologies. IEEE.

[92]

Kai Zhao, Wenzhe Zhao, Hongbin Sun, Xiaodong Zhang, Nanning Zheng, and Tong Zhang. 2013. LDPC-in-SSD: Making advanced error correction codes work effectively in solid state drives. In Proceedings of the 11th USENIX Conference on File and Storage Technologies.

[93]

Mark Zhao, Niket Agarwal, Aarti Basant, Buğra Gedik, Satadru Pan, Mustafa Ozdal, Rakesh Komuravelli, Jerry Pan, Tianshu Bao, Haowei Lu, et al. 2022. Understanding data storage and ingestion for large-scale deep recommendation model training: Industrial product. In Proceedings of the 49th Annual International Symposium on Computer Architecture. 1042–1057.

Digital Library

[94]

Xiaoyong Zhu. (n.d.). Feathr: LinkedIn’s feature store is now available on Azure. https://azure.microsoft.com/en-us/blog/feathr-linkedin-s-feature-store-is-now-available-on-azure/.

Cited By

Yao YWang XZhou DLi LWu JZhu LWang ZLuo Y(2024)EKRM: Efficient Key-Value Retrieval Method to Reduce Data Lookup Overhead for RedisEuro-Par 2024: Parallel Processing10.1007/978-3-031-69577-3_12(166-179)Online publication date: 26-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-69577-3_12

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Realizing Strong Determinism Contract on Log-Structured Merge Key-Value Stores

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Published In

cover image ACM Transactions on Storage

ACM Transactions on Storage Volume 19, Issue 2

May 2023

269 pages

ISSN:1553-3077

EISSN:1553-3093

DOI:10.1145/3585541

Editor:
Erez Zadok
Stony Brook University, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2023

Online AM: 24 February 2023

Accepted: 23 January 2023

Received: 30 December 2022

Published in TOS Volume 19, Issue 2

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Yao YWang XZhou DLi LWu JZhu LWang ZLuo Y(2024)EKRM: Efficient Key-Value Retrieval Method to Reduce Data Lookup Overhead for RedisEuro-Par 2024: Parallel Processing10.1007/978-3-031-69577-3_12(166-179)Online publication date: 26-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-69577-3_12

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