Award Abstract # 2145713
CAREER: Achieving Ultra-Low Latency under Heterogeneity and Uncertainty in Edge Computing

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: CARNEGIE MELLON UNIVERSITY
Initial Amendment Date: April 22, 2022
Latest Amendment Date: July 24, 2023
Award Number: 2145713
Award Instrument: Continuing Grant
Program Manager: Huaiyu Dai
hdai@nsf.gov
 (703)292-4568
ECCS
 Division of Electrical, Communications and Cyber Systems
ENG
 Directorate for Engineering
Start Date: April 1, 2022
End Date: March 31, 2027 (Estimated)
Total Intended Award Amount: $500,000.00
Total Awarded Amount to Date: $500,000.00
Funds Obligated to Date: FY 2022 = $393,966.00
FY 2023 = $106,034.00
History of Investigator:
  • Weina Wang (Principal Investigator)
    weinaw@cs.cmu.edu
Recipient Sponsored Research Office: Carnegie-Mellon University
5000 FORBES AVE
PITTSBURGH
PA  US  15213-3890
(412)268-8746
Sponsor Congressional District: 12
Primary Place of Performance: Carnegie-Mellon University
PA  US  15213-3815
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): U3NKNFLNQ613
Parent UEI: U3NKNFLNQ613
NSF Program(s): CCSS-Comms Circuits & Sens Sys
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1045, 153E, 9102
Program Element Code(s): 756400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Edge computing has been envisioned to be a paradigm beyond cloud computing that supports emerging applications such as autonomous driving, augmented reality, and automated mobile robots. However, to realize the envisioned latency breakthrough of edge computing and put this new paradigm into operation, a critical piece that is still missing is algorithms that orchestrate the data and the computation to guarantee ultra-low latency. The overall objective of this CAREER proposal is to fill this gap by developing (i) orchestration algorithms that dynamically coordinate data and computation for edge computing systems to meet stringent latency goals, and (ii) theoretical foundations to characterize the fundamental resource requirements and optimal operating points of edge computing systems. The algorithmic innovation and provisioning insights for achieving ultra-low latency in this proposal will guide the deployment of edge computing systems in large scale, greatly benefiting latency sensitive edge applications with strong societal impacts such as cognitive assistance for the elderly and disabled and autonomous driving. The theoretical advances under this proposal will make fundamental contributions to research in stochastic systems, creating new research focuses for interdisciplinary research communities at the intersection of electrical engineering, computer science, and operations research. This proposal will have significant educational and community impact. Both the theoretical approaches and the experiment platforms will be incorporated into the curriculum and course projects at graduate and undergraduate levels at Carnegie Mellon University. Online platforms will also be leveraged to disseminate educational and research materials related to this project for a greater reach. Continuing and expanded efforts will be spent on STEM outreach activities to K-12 students, mentoring students from underrepresented groups for research, promoting the visibility of researchers from underrepresented groups, and initiating online seminars to outreach to the general public.

The goal of this project is to develop (i) orchestration algorithms that dynamically coordinate data and computation for edge computing systems to meet stringent latency goals, and (ii) theoretical foundations to characterize the fundamental resource requirements and optimal operating points of edge computing systems. In particular, this goal will be achieved in two representative operating modes of edge systems (Thrusts I and II), respectively, based on which edge nodes are authorized to process the data generated by clients and whose computing power is being exploited. Then the uncertainty in communication and computation environments will be addressed in an orthogonal thrust (Thrust III) learning-based orchestration. The proposed research will result in the currently missing algorithmic innovation and provisioning insights needed for guaranteeing ultra-low latency in edge computing systems. Specifically, orchestration algorithms will be developed to jointly and dynamically utilize the communication resources under the emerging 5G and beyond wireless technologies and the dispersed computing power of edge servers and edge clients. The cross-cutting approach in this proposal is motivated by the observation that future edge systems will be of large scale, and the approach builds upon significant recent results on large-scale stochastic systems. These results demonstrate that with the right orchestration algorithms, it is possible to achieve ultra-low latency and high system utilization simultaneously in large systems. This proposal will further advance the theory for large-scale stochastic systems to a much greater generality to address heterogeneity, uncertainty, interactions among different types of resources, and dynamic performance-based job execution. These are new unique challenges arising in edge systems and modern applications in general that are highly underexplored in traditional approaches.

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 11)
Choudhury, Tuhinangshu and Joshi, Gauri and Wang, Weina "Job assignment in machine learning inference systems with accuracy constraints" Performance Evaluation , v.167 , 2025 https://doi.org/10.1016/j.peva.2024.102463 Citation Details
Choudhury, Tuhinangshu and Wang, Weina and Joshi, Gauri "Tackling heterogeneous traffic in multi-access systems via erasure coded servers" Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing , 2022 https://doi.org/10.1145/3492866.3549713 Citation Details
Hong, Yige and Wang, Weina "Sharp waiting-time bounds for multiserver jobs" Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing , 2022 https://doi.org/10.1145/3492866.3549717 Citation Details
Hong, Yige and Xie, Qiaomin and Wang, Weina "Near-Optimal Stochastic Bin-Packing in Large Service Systems with Time-Varying Item Sizes" Proceedings of the ACM on Measurement and Analysis of Computing Systems , v.7 , 2023 https://doi.org/10.1145/3626779 Citation Details
Hong, Yige Hong and Xie, Qiaomin and Chen, Yudong and Wang, Weina "Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption" , 2023 Citation Details
Jali, Neharika and Qu, Guannan and Wang, Weina and Joshi, Gauri "Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems" , 2024 Citation Details
Wang, Ziao and Wang, Weina and Wang, Lele "Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation" , 2024 Citation Details
Wang, Ziao and Zhang, Ning and Wang, Weina and Wang, Lele "On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment" IEEE Transactions on Information Theory , v.70 , 2024 https://doi.org/10.1109/TIT.2024.3351107 Citation Details
Williams, Jalani K. and Harchol-Balter, Mor and Wang, Weina "The M/M/k with Deterministic Setup Times" Proceedings of the ACM on Measurement and Analysis of Computing Systems , v.6 , 2022 https://doi.org/10.1145/3570617 Citation Details
Zhang, Ning and Wang, Ziao and Wang, Weina and Wang, Lele "Attributed Graph Alignment" IEEE Transactions on Information Theory , v.70 , 2024 https://doi.org/10.1109/TIT.2024.3403810 Citation Details
Zhang, Qining and Wei, Honghao and Wang, Weina and Ying, Lei "On low-complexity quickest intervention of mutated diffusion processes through local approximation" Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing , 2022 https://doi.org/10.1145/3492866.3549709 Citation Details
(Showing: 1 - 10 of 11)

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