| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | February 5, 2021 |
| Latest Amendment Date: | May 21, 2021 |
| Award Number: | 2112694 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Alhussein Abouzeid
aabouzei@nsf.gov (703)292-7855 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2020 |
| End Date: | April 30, 2023 (Estimated) |
| Total Intended Award Amount: | $496,790.00 |
| Total Awarded Amount to Date: | $301,495.00 |
| Funds Obligated to Date: |
FY 2019 = $89,495.00 FY 2020 = $115,690.00 FY 2021 = $93,954.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
300 TURNER ST NW BLACKSBURG VA US 24060-3359 (540)231-5281 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
VA US 24061-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Special Projects - CNS, Networking Technology and Syst |
| Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
With the advent of smart devices and the Internet of things, wireless technology has spawned a plethora of services that span business, science and engineering, entertainment, safety and security, health monitoring, and cover a large portion of our social interactions. Due to the prevalence of these new services, today's wireless networks are witnessing not only an unprecedented growth in the volume of traffic, but also a significant change in the types of traffic (e.g., a much higher percentage of voice/video traffic with more stringent delay requirements). These new trends require next-generation wireless networks to provide not only high data rates (tens of gigabits per second), but also ultra-low latencies (sub-millisecond). Moreover, as wireless networks grow and support an increasingly large number of users, network control algorithms must also incur low complexity in order to be implemented in practice. However, the question of how to simultaneously achieve high throughput, low delay and low complexity remains largely open. Addressing this major research challenge is a main goal of this project. Not only is this research expected to substantially advance our understanding of designing efficient control algorithms for wireless networks with jointly optimized performance, but it would also expand/create the much-needed theoretical foundations for developing simple and practical protocols to optimize the key performance metrics needed in the design of next-generation wireless networks. This research will also be closely integrated with a comprehensive educational plan, which is focused on providing research experiences to undergraduate and K-12 students, recruiting and training underrepresented students, and engaging in curriculum development activities.
The goal of this project is to create new theoretical foundations for designing provably efficient network control algorithms that perform well in all three dimensions of throughput, delay, and complexity. Specifically, this research will be carried out around three main thrusts: (i) it focuses on intra-cell control for a multi-channel cellular network, and aims to build a theoretical framework for designing low-complexity scheduling algorithms with provably guaranteed optimal throughput and optimal (or near-optimal) large-deviations delay rate-function; (ii) it considers a more challenging setting of network-wide control for larger systems (e.g., a dense multi-cell system or an ad hoc wireless network), and aims to develop a new node-based approach for designing efficient scheduling algorithms with provable throughput and evacuation time performance; and (iii) it considers distributed network-side control and aims to design low-complexity algorithms that achieve high throughput and low delay.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
In this project, our goal was to create new theoretical foundations for designing provably efficient network control and resource allocation algorithms that perform well in all dimensions of throughput, delay, and complexity.
The project delivered new modeling techniques, network control and resource allocation algorithms, and theoretical analyses to advance the area of network control and resource allocation. We highlight the following main contributions. (1) The team studied the design of uplink scheduling algorithms for large-scale wireless Internet-of-Things (IoT) systems. We developed an efficient and low-overhead scheduling algorithm that can strictly satisfy the sampling constraint with asymptotically diminishing throughput loss. (2) The team investigated the design of provably efficient online scheduling algorithms for wireless networks. We developed novel node-based scheduling algorithms that have provable performance guarantees for both throughput and evacuation time. (3) The team considered a newly proposed metric called age-of-information (AoI), which is a broader metric that includes delay and is particularly important for real-time applications. We investigated several new problems focused on AoI optimization and developed control and scheduling algorithms with AoI guarantees. (4) The team investigated efficient and robust management of virtual network functions. By leveraging submodular optimization and novel relaxation and rounding techniques, we developed provably efficient algorithms for allocating virtual network resources to maximize the throughput or to minimize the delay, which play an important role in network function virtualization enabled 5G networks and beyond. (5) The team considered the integration of online learning techniques with network control and resource allocation in fast-changing, highly uncertain network environments. Motivated by several important networking problems, e.g., cellular network configuration and timely throughput maximization for real-time wireless traffic, we developed joint learning and control algorithms and rigorously proved performance guarantees for throughput, constraint violation, communication cost, and privacy protection.
This project engaged a team consisting of six Ph.D. students and over ten undergraduate students, including five individuals who identify as women and/or come from under-represented minority backgrounds. As a result of this project's support, three Ph.D. dissertations were successfully completed. The contributions made by this project have had significant and noteworthy impacts on various research communities, spanning areas from networking and communications to control and machine learning. These impactful contributions have led to recognition in the form of prestigious awards, including the IEEE INFOCOM 2019 Best Paper Award and the IEEE/IFIP WiOpt 2022 Best Student Paper Award. Moreover, both the PI and his students have received multiple faculty and student awards for their outstanding work and achievements.
Last Modified: 06/05/2023
Modified by: Bo Ji
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