Award Abstract # 2337806
CAREER: The Case for Disaggregated Database Systems

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: PURDUE UNIVERSITY
Initial Amendment Date: March 20, 2024
Latest Amendment Date: March 20, 2024
Award Number: 2337806
Award Instrument: Continuing Grant
Program Manager: Sorin Draghici
sdraghic@nsf.gov
 (703)292-2232
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: April 1, 2024
End Date: March 31, 2029 (Estimated)
Total Intended Award Amount: $534,270.00
Total Awarded Amount to Date: $176,309.00
Funds Obligated to Date: FY 2024 = $176,309.00
History of Investigator:
  • Jianguo Wang (Principal Investigator)
Recipient Sponsored Research Office: Purdue University
2550 NORTHWESTERN AVE # 1100
WEST LAFAYETTE
IN  US  47906-1332
(765)494-1055
Sponsor Congressional District: 04
Primary Place of Performance: Purdue University
2550 NORTHWESTERN AVE STE 1900
WEST LAFAYETTE
IN  US  47906-1332
Primary Place of Performance
Congressional District:
04
Unique Entity Identifier (UEI): YRXVL4JYCEF5
Parent UEI: YRXVL4JYCEF5
NSF Program(s): Info Integration & Informatics
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
01002526DB NSF RESEARCH & RELATED ACTIVIT

01002627DB NSF RESEARCH & RELATED ACTIVIT

01002728DB NSF RESEARCH & RELATED ACTIVIT

01002829DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1045, 7364
Program Element Code(s): 736400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Database systems are fundamental to numerous mission-critical applications, such as finance, e-commerce, and transportation, because they efficiently manage large-scale data. For decades, database systems have been built for monolithic servers, where compute, memory, and storage are tightly integrated. However, these traditional databases now struggle to meet the stringent requirements of elasticity, scalability, and cost-effectiveness, especially when supporting large-scale applications in the cloud. Recently, there has been an emerging technology trend towards hardware resource disaggregation, which involves physically separating the hardware resources (such as compute, memory, and storage) into distinct resource pools to enable independent and elastic resource scaling. However, this disaggregated architecture presents fundamental challenges for traditional databases. This project will build a new database system that is specifically optimized for resource disaggregation to substantially improve performance, scalability, and elasticity. This will, in turn, significantly reduce costs for database customers and yield substantial economic benefits for society. The developed techniques will be open-sourced to enhance the research infrastructure. Research findings will be disseminated through publications at top-tier venues and will be incorporated into both graduate-level and undergraduate-level database courses at Purdue University. Furthermore, this project will foster diversity and inclusion by actively engaging groups that are traditionally underrepresented in this field, such as women, minority groups, and underprivileged students.

Specifically, this project investigates the profound impact of resource disaggregation (with respect to both storage and memory) on database systems. It presents a new disaggregated database system through three key thrusts. The first thrust introduces new approaches to efficiently manage database logs and perform transaction commits for storage disaggregation. The second thrust proposes new techniques to optimize database indexes and the buffer manager for memory disaggregation. The third thrust develops a new distributed database architecture for memory disaggregation and redesigns concurrency control and crash recovery. Overall, this project will drive the next wave of innovation in the field of database systems by starting a new line of research on disaggregated data(base) systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chen, Cheng and Jin, Chenzhe and Zhang, Yunan and Podolsky, Sasha and Wu, Chun and Wang, Szu-Po and Hanson, Eric and Sun, Zhou and Walzer, Robert and Wang, Jianguo "SingleStore-V: An Integrated Vector Database System in SingleStore" Proceedings of the VLDB Endowment , v.17 , 2024 https://doi.org/10.14778/3685800.3685805 Citation Details
Pang, Xi and Wang, Jianguo "Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases" Proceedings of the ACM on Management of Data , v.2 , 2024 https://doi.org/10.1145/3654983 Citation Details
Su, Yongye and Sun, Yinqi and Zhang, Minjia and Wang, Jianguo "Vexless: A Serverless Vector Data Management System Using Cloud Functions" Proceedings of the ACM on Management of Data , v.2 , 2024 https://doi.org/10.1145/3654990 Citation Details
Wang, Ruihong and Gao, Chuqing and Wang, Jianguo and Kadam, Prishita and TamerÖzsu, M and Aref, Walid G "Optimizing LSM-based indexes for disaggregated memory" The VLDB Journal , 2024 https://doi.org/10.1007/s00778-024-00863-y Citation Details
Yu, Qiaolin and Guo, Chang and Zhuang, Jay and Thakkar, Viraj and Wang, Jianguo and Cao, Zhichao "CaaS-LSM: Compaction-as-a-Service for LSM-based Key-Value Stores in Storage Disaggregated Infrastructure" Proceedings of the ACM on Management of Data , v.2 , 2024 https://doi.org/10.1145/3654927 Citation Details

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