Award Abstract # 2107147
Collaborative Research: CNS Core: Medium: Movement of Computation and Data in Splitkernel-disaggregated, Data-intensive Systems

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA, THE
Initial Amendment Date: June 11, 2021
Latest Amendment Date: September 7, 2022
Award Number: 2107147
Award Instrument: Continuing Grant
Program Manager: Marilyn McClure
mmcclure@nsf.gov
 (703)292-5197
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: June 15, 2021
End Date: May 31, 2025 (Estimated)
Total Intended Award Amount: $898,087.00
Total Awarded Amount to Date: $898,087.00
Funds Obligated to Date: FY 2021 = $595,456.00
FY 2022 = $302,631.00
History of Investigator:
  • Vincent Liu (Principal Investigator)
    liuv@seas.upenn.edu
  • Boon Thau Loo (Co-Principal Investigator)
  • Sebastian Angel (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Pennsylvania
3451 WALNUT ST STE 440A
PHILADELPHIA
PA  US  19104-6205
(215)898-7293
Sponsor Congressional District: 03
Primary Place of Performance: University of Pennsylvania
3330 Walnut Street Levine Hall
Philadelphia
PA  US  19104-6202
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): GM1XX56LEP58
Parent UEI: GM1XX56LEP58
NSF Program(s): CSR-Computer Systems Research
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7924
Program Element Code(s): 735400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Resource disaggregation promises to change the way in which we design and operate cloud infrastructure. But despite its positive impact on the operation and cost-efficiency of data centers, disaggregation comes with overheads that arise from introducing a PCIe and network round trip for each access to remote memory, storage, or accelerators -- overheads that are particularly pronounced for data-intensive applications. The core research question that this proposal will answer is what the advent of disaggregation implies for the decades of architectural decisions made by the designers of data-intensive systems, and likewise, what OS-level primitives are required to adapt these systems to a disaggregated setting. To that end, this work proposes a co-design of disaggregated operating systems and middleware for data-intensive systems that achieves both high performance and ease of use,

Data-intensive systems like the ones targeted by this work power much of today?s world, providing analytics for everything from business informatics to Internet search and healthcare. The proposed work will assist data center operators in transitioning their infrastructure to support resource disaggregation while minimizing performance overheads to applications written in common frameworks.

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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(Showing: 1 - 10 of 12)
Wu, Chenyuan and Qin, Haoyun and Amiri, Mohammad_Javad and Loo, Boon_Thau and Malkhi, Dahlia and Marcus, Ryan "BFTBrain: Adaptive BFT Consensus with Reinforcement Learning" , 2025 Citation Details
Zhang, Haoran and Kallas, Konstantinos and Pavlatos, Spyros and Alur, Rajeev and Angel, Sebastian and Liu, Vincent "MuCache: a General Framework for Caching in Microservice Graphs" , 2024 Citation Details
Shen, Weihai and Khanna, Ansh and Angel, Sebastian and Sen, Siddhartha and Mu, Shuai "Rolis: a software approach to efficiently replicating multi-core transactions" Proceedings of the European Conference on Computer Systems (EuroSys) , 2022 https://doi.org/10.1145/3492321.3519561 Citation Details
Ng, Kelvin_K W and Demoulin, Henri Maxime and Liu, Vincent "Paella: Low-latency Model Serving with Software-defined GPU Scheduling" , 2023 https://doi.org/10.1145/3600006.3613163 Citation Details
Shen, Weihai and Cui, Yang and Sen, Siddhartha and Angel, Sebastian and Mu, Shuai "Mako: Speculative Distributed Transactions with Geo-Replication" , 2025 Citation Details
Liu, Xuting and Arzani, Behnaz and Kakarla, Siva_Kesava Reddy and Zhao, Liangyu and Liu, Vincent and Castro, Miguel and Kandula, Srikanth and Marshall, Luke "Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem" , 2024 https://doi.org/10.1145/3651890.3672249 Citation Details
Ioannidis, Eleftherios and Zakowski, Yannick and Zdancewic, Steve and Angel, Sebastian "Structural temporal logic for mechanized program verification" Proceedings of the ACM on programming languages , 2025 Citation Details
Zhang, Q and Chen, X and Sankhe, S and Zheng Z and Zhong K and Angel S and Chen, A and Liu V and Loo B "Optimizing Data-intensive Systems in Disaggregated Data Centers with TELEPORT" ACM SIGMOD , 2022 https://doi.org/10.1145/3514221.3517856 Citation Details
Zhang, Haoran and Mu, Shuai and Angel, Sebastian and Liu, Vincent "CausalMesh: A Causal Cache for Stateful Serverless Computing" Proceedings of the VLDB Endowment , 2024 Citation Details
Chen, Xinyi and Yu, Liangcheng and Liu, Vincent and Zhang, Qizhen "Cowbird: Freeing CPUs to Compute by Offloading the Disaggregation of Memory" , 2023 https://doi.org/10.1145/3603269.3604833 Citation Details
Amiri, Mohammad Javad and Wu, Chenyuan and Agrawal, Divyakant and El_Abbadi, Amr and Loo, Boon Thau and Sadoghi, Mohammad "The Bedrock of Byzantine Fault Tolerance: A Unified Platform for BFT Protocols Analysis, Implementation, and Experimentation" , 2024 Citation Details
(Showing: 1 - 10 of 12)

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