research-article

ISKEVA: in-SSD key-value database engine for video analytics applications

Authors:

Joshua Fixelle,

Nagadastagiri Challapalle,

Vijaykrishnan NarayananAuthors Info & Claims

LCTES 2022: Proceedings of the 23rd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems

Pages 50 - 60

https://doi.org/10.1145/3519941.3535068

Published: 14 June 2022 Publication History

Abstract

Key-value databases are widely used to store the features or metadata generated from the neural network based video processing platforms. Due to the large volumes of video data, these databases use solid state drives (SSDs) as the primary data storage platform, and user query-based filtering, and retrieval operations on data incur large volume of data movement between the SSD and the host processor. In this paper, we present an in-SSD key-value database which uses the embedded CPU core, and DRAM memory on the SSD to support various queries with predicates and reduce the data movement between SSD and host processor significantly. We augment the SSD flash translation layer with key-value database functions and auxiliary data structures to support the user queries using the embedded core and DRAM memory on SSD. The proposed key-value store prototype on the Cosmos plus OpenSSD board reduces data movement between host processor and SSD by 14.57x, achieves an application-level speedup by 1.16x, and reduced energy consumption by 56% across different types of user queries.

References

[1]

Key Value Storage API Specification Version 1. 1. SNIA. https://www.snia.org/sites/default/files/technical_work/KVSAPI/ Key%20Value %20Storage%20API%20v1.1.pdf

[2]

Microsoft-Rocket-Video-Analytics-Platform. Microsoft. https:// github.com/microsoft/Microsoft-Rocket-Video-Analytics-Platform

[3]

Oracle In-Memory Database Cache. Oracle. https://www.oracle.com/ technetwork/database/windows/ds-imdb-cache-1-129794.pdf

[4]

rapl-tool. kentcz. https://github.com/kentcz/rapl-tools

[5]

Samsung SmartSSD. Samsung. https://samsungsemiconductor-us. com/smartssd/

[6]

Sanjay Ghemawat and Jef Dean. LevelDB. https://github.com/google/ leveldb

[7]

Xilinx Vivado. AMD Xilinx. https://www.xilinx.com/products/designtools/vivado.html

[8]

Xilinx XPE. AMD Xilinx. https://www.xilinx.com/products/ technology/power/xpe.html

[9]

Zynq7000. Xilinx. https://www.xilinx.com/support/documentation/ data_sheets/ds190-Zynq-7000-Overview.pdf

[10]

Ganesh Ananthanarayanan et al. 2020. Live Video Analytics with Microsoft Rocket for reducing edge compute costs.

[11]

Esmail Asyabi, Azer Bestavros, Erfan Sharafzadeh, and Timothy Zhu. 2020. Peafowl: In-Application CPU Scheduling to Reduce Power Consumption of in-Memory Key-Value Stores. In Proceedings of the 11th ACM Symposium on Cloud Computing (Virtual Event, USA) ( SoCC '20). Association for Computing Machinery, New York, NY, USA, 150-164. https://doi.org/10.1145/3419111.3421298

Digital Library

[12]

Tim Bisson, Ke Chen, Changho Choi, Vijay Balakrishnan, and Yangsuk Kee. 2018. Crail-KV: A High-Performance Distributed Key-Value Store Leveraging Native KV-SSDs over NVMe-oF. In 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). 1-8. https://doi.org/10.1109/PCCC. 2018.8710776

[13]

Asaf Cidon, Daniel Rushton, Stephen M. Rumble, and Ryan Stutsman. 2017. Memshare: a Dynamic Multi-tenant Key-value Cache. In 2017 USENIX Annual Technical Conference (USENIX ATC 17). USENIX Association, Santa Clara, CA, 321-334. https://www.usenix.org/conference/ atc17/technical-sessions/presentation/cidon

Digital Library

[14]

Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. 2018. Scaling Egocentric Vision: The EPIC-KITCHENS Dataset. In European Conference on Computer Vision (ECCV). https://doi.org/10.48550/arXiv. 1804.02748

[15]

Biplob Debnath, Sudipta Sengupta, and Jin Li. 2010. FlashStore: High Throughput Persistent Key-Value Store. Proc. VLDB Endow. 3, 1-2 (sep 2010 ), 1414-1425. https://doi.org/10.14778/1920841.1921015

Digital Library

[16]

Diego Didona and Willy Zwaenepoel. 2019. Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores. In 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19). USENIX Association, Boston, MA, 79-94. https://www. usenix.org/conference/nsdi19/presentation/didona

[17]

Junsu Im, Jinwook Bae, Chanwoo Chung, Arvind, and Sungjin Lee. 2021. Design of LSM-Tree-Based Key-Value SSDs with Bounded Tails. ACM Trans. Storage 17, 2, Article 10 (may 2021 ), 27 pages. https://doi.org/10.1145/3452846

Digital Library

[18]

Jingwei Ji, Ranjay Krishna, Li Fei-Fei, and Juan Carlos Niebles. 2020. Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10236-10247.

[19]

Yanqin Jin, Hung-Wei Tseng, Yannis Papakonstantinou, and Steven Swanson. 2017. KAML: A Flexible, High-Performance Key-Value SSD. In 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA). 373-384. https://doi.org/10.1109/HPCA. 2017.15

[20]

Yangwook Kang et al. 2019. Towards Building a High-Performance, Scale-in Key-Value Storage System. In Proceedings of the 12th ACM International Conference on Systems and Storage (Haifa, Israel) (SYSTOR '19). Association for Computing Machinery, 144-154. https://doi.org/ 10.1145/3319647.3325831

Digital Library

[21]

Jaewook Kwak, Sangjin Lee, Kibin Park, Jinwoo Jeong, and Yong Ho Song. 2020. Cosmos+ OpenSSD: Rapid Prototype for Flash Storage Systems. ACM Trans. Storage 16, 3, Article 15 (jul 2020 ), 35 pages. https://doi.org/10.1145/3385073

Digital Library

[22]

Hyeontaek Lim, Dongsu Han, David G. Andersen, and Michael Kaminsky. 2014. MICA: A Holistic Approach to Fast In-Memory KeyValue Storage. In 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14). USENIX Association, Seattle, WA, 429-444. https://www.usenix.org/conference/nsdi14/technicalsessions/presentation/lim

[23]

Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. 2014. Microsoft COCO : Common Objects in Context. https://doi.org/10.48550/ARXIV.1405.0312

[24]

Vikram Sharma Mailthody, Zaid Qureshi, Weixin Liang, Ziyan Feng, Simon Garcia de Gonzalo, Youjie Li, Hubertus Franke, Jinjun Xiong, Jian Huang, and Wen-mei Hwu. 2019. DeepStore: In-Storage Acceleration for Intelligent Queries. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (Columbus, OH, USA) ( MICRO '52). Association for Computing Machinery, New York, NY, USA, 224-238. https://doi.org/10.1145/3352460.3358320

Digital Library

[25]

Sara McAllister, Benjamin Berg, Julian Tutuncu-Macias, Juncheng Yang, Sathya Gunasekar, Jimmy Lu, Daniel S. Berger, Nathan Beckmann, and Gregory R. Ganger. 2021. Kangaroo: Caching Billions of Tiny Objects on Flash. In Proceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles (Virtual Event, Germany) (SOSP '21). Association for Computing Machinery, New York, NY, USA, 243-262. https://doi.org/10.1145/3477132.3483568

Digital Library

[26]

Cheng Pan et al. 2019. Lightweight and Accurate Memory Allocation in Key-Value Cache. International Journal of Parallel Programming 47, 3 ( 06 2019 ), 451-466. https://doi.org/10.1007/s10766-018-0616-4

Digital Library

[27]

Zhenyuan Ruan, Tong He, and Jason Cong. 2019. INSIDER: Designing in-Storage Computing System for Emerging High-Performance Drive. In Proceedings of the 2019 USENIX Conference on Usenix Annual Technical Conference (Renton, WA, USA) ( USENIX ATC '19). USENIX Association, USA, 379-394.

[28]

Sahand Salamat, Armin Haj Aboutalebi, Behnam Khaleghi, Joo Hwan Lee, Yang Seok Ki, and Tajana Rosing. 2021. NASCENT: Near-Storage Acceleration of Database Sort on SmartSSD. In The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (Virtual Event, USA) (FPGA '21). Association for Computing Machinery, New York, NY, USA, 262-272. https://doi.org/10.1145/3431920.3439298

Digital Library

[29]

Mohamed Sarwat, Raha Morafah, Mohamed F. Mokbel, and James L. Avery. 2017. Database System Support for Personalized Recommendation Applications. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). 1320-1331. https://doi.org/10.1109/ICDE. 2017.174

[30]

Debra T. Cohen Seh Kwa. 2002. PCI Express* Architecture Power Management Rev. 1.1. Intel. https://www.intel.com/content/www/us/ en/io/pci-express/ pci-express-architecture-power-management-rev1-1-paper.html

[31]

Fadime Sener and Angela Yao. 2018. Zero-Shot Anticipation for Instructional Activities. CoRR abs/ 1812.02501 ( 2018 ). arXiv: 1812.02501 http://arxiv.org/abs/ 1812.02501

[32]

Huijuan Xu, Kun He, Bryan A. Plummer, Leonid Sigal, Stan Sclarof, and Kate Saenko. 2019. Multilevel Language and Vision Integration for Text-to-Clip Retrieval. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI'19/IAAI'19/EAAI'19). AAAI Press, Article 1112, 8 pages. https://doi.org/10.1609/aaai.v33i01. 33019062

Digital Library

[33]

Juncheng Yang, Yao Yue, and Rashmi Vinayak. 2021. Segcache: a memory-eficient and scalable in-memory key-value cache for small objects. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association, 503-518. https://www.usenix.org/conference/nsdi21/presentation/yang-juncheng

[34]

Fisher Yu et al. 2020. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. arXiv: 1805. 04687 [cs.CV]

[35]

Peng Zhou, Mei Li, Jing Huang, and Hua Fang. 2014. Research on Database Schema Comparison of Relational Databases and Key-Value Stores. In Modern Technologies in Materials, Mechanics and Intelligent Systems (Advanced Materials Research, Vol. 1049 ). Trans Tech Publications Ltd, 1860-1863.

Cited By

Zhang XBhimani JPei SLee ELee SSeong YKim EChoi CNam EChoi JKim B(2025)Storage Abstractions for SSDs: The Past, Present, and FutureACM Transactions on Storage10.1145/370899221:1(1-44)Online publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1145/3708992
Park CLee JLiu CKang KKandemir MChoi WEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)AnyKey: A Key-Value SSD for All Workload TypesProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707279(47-63)Online publication date: 30-Mar-2025
https://dl.acm.org/doi/10.1145/3669940.3707279
Zheng YFixelle JHuo PStan MMesnier MNarayanan VEgger BLee D(2023)ISVABI: In-Storage Video Analytics Engine with Block InterfaceProceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3589610.3596275(111-121)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1145/3589610.3596275

Index Terms

ISKEVA: in-SSD key-value database engine for video analytics applications
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Firmware

Recommendations

An Efficient Memory-Mapped Key-Value Store for Flash Storage
SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

Persistent key-value stores have emerged as a main component in the data access path of modern data processing systems. However, they exhibit high CPU and I/O overhead. Today, due to power limitations it is important to reduce CPU overheads for data ...
Evaluation of Exclusive Data Allocation Between SSD Tier and SSD Cache in Storage Systems
ICEIS 2014: Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1

We proposed and evaluated the storage I/O response time with the exclusive allocation method between SSD for tiered volume and SSD for cache in the storage system utilizing SSD and HDD. In the proposed method, the SSD cache function with exclusive ...
Does RAID Improve Lifetime of SSD Arrays?

Parity protection at the system level is typically employed to compose reliable storage systems. However, careful consideration is required when SSD-based systems employ parity protection. First, additional writes are required for parity updates. Second,...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

LCTES 2022: Proceedings of the 23rd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems

June 2022

161 pages

ISBN:9781450392662

DOI:10.1145/3519941

General Chair:
Tobias Grosser
University of Edinburgh, UK
,
Program Chair:
Kyoungwoo Lee
Yonsei University, South Korea

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

LCTES '22

Sponsor:

LCTES '22: 23rd ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems

June 14, 2022

CA, San Diego, USA

Acceptance Rates

Overall Acceptance Rate 116 of 438 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
419
Total Downloads

Downloads (Last 12 months)51
Downloads (Last 6 weeks)8

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang XBhimani JPei SLee ELee SSeong YKim EChoi CNam EChoi JKim B(2025)Storage Abstractions for SSDs: The Past, Present, and FutureACM Transactions on Storage10.1145/370899221:1(1-44)Online publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1145/3708992
Park CLee JLiu CKang KKandemir MChoi WEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)AnyKey: A Key-Value SSD for All Workload TypesProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707279(47-63)Online publication date: 30-Mar-2025
https://dl.acm.org/doi/10.1145/3669940.3707279
Zheng YFixelle JHuo PStan MMesnier MNarayanan VEgger BLee D(2023)ISVABI: In-Storage Video Analytics Engine with Block InterfaceProceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems10.1145/3589610.3596275(111-121)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1145/3589610.3596275

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten