Projects | Networks Research Group

NeTS: Small: NSF-DST: Integrating Physical and Network Layers in the Design of Multi-Core Coherent Optical WDM Networks

Mon, 01 Jul 2024 00:00:00 +0000

Award Number: 2413851
Funding Agency: National Science Foundation (NSF)
Award Amount: $599,996
Project Duration: July 1, 2024 - June 30, 2027 (Estimated)

Dr. Byrav Ramamurthy has secured a significant grant from the National Science Foundation (NSF) for his project, “Integrating Physical and Network Layers in the Design of Multi-Core Coherent Optical WDM Networks.”

This groundbreaking research aims to advance our understanding of multi-core coherent optical communication networks by integrating high-fidelity physical layer models with efficient network layer algorithms, informed by lab and field measurements. The project, in collaboration with the Indian Institute of Technology - Madras (IITM), will focus on improving optical networking technologies by bridging the gap between physical and network layers.

Key objectives of the project include:

Optical Phase Conjugation Implementation: Demonstrating the feasibility of Optical Phase Conjugators in a field testbed and incorporating physical layer models into network simulations. Space Division Multiplexing (SDM): Implementing SDM in four-core fibers in a lab environment and integrating these models into network layer simulations. Resource Allocation Algorithms: Developing efficient resource allocation algorithms for multi-core networks, exploring both direct and coherent detection mechanisms, and optical phase conjugation techniques.

This research promises to transform optical networking design and planning by incorporating physical layer characteristics like signal degradation, noise, and interference into network simulations. These enhancements will significantly improve performance metrics such as throughput, latency, and reliability, ensuring efficient resource utilization in modern telecommunication systems.

The project is part of NSF’s mission to support innovative research with substantial intellectual merit and broader impacts, recognizing Dr. Ramamurthy’s leadership in advancing optical networking technologies.

Data-driven Machine Learning and AI-based Data Transfer Strategies for High Energy Physics Experiments (HEP) on Open Science Grid (OSG)

Tue, 15 Aug 2023 00:00:00 +0000

Award Number: DE-SC0024648
Funding Agency: U.S. Department of Energy (DoE)
Award Amount: Not specified
Project Duration: August 15, 2023 - August 14, 2026

Dr. Byrav Ramamurthy, in collaboration with Dr. Derek Weitzel, has secured a significant grant from the U.S. Department of Energy for the project, “Data-driven Machine Learning and AI-based Data Transfer Strategies for High Energy Physics Experiments (HEP) on Open Science Grid (OSG).”

This project addresses the rapidly growing data demands of High Energy Physics (HEP) experiments, which require high-rate data transfer capabilities and efficient storage and networking solutions. As the volume of data from HEP experiments is expected to grow exponentially, the existing infrastructure risks being overwhelmed, creating bottlenecks that could impede the seamless execution of these workflows.

Key objectives of the project include: Machine Learning (ML) and AI-based Strategies: Designing online and offline ML/AI-based strategies to optimize HEP data transfers, enhancing the speed and efficiency of data distribution among computing facilities. Data Log Analysis: Conducting post-hoc and real-time analysis of cache transfer logs and network/storage resource data at HEP experiment endpoints to identify and resolve bottlenecks. Data Storage Mechanisms: Developing new data formats and storage mechanisms for faster querying and analysis using the University of Nebraska’s Holland Computing Center (HCC) and the Open Science Grid (OSG) infrastructure.

This research will leverage the high-performance computing resources at HCC, and the findings will be deployed on the OSG endpoint, benefiting numerous HEP experiments. By implementing intelligent data transfer strategies, the project aims to alleviate the bottlenecks in current storage, compute, and network infrastructures.

This project is supervised by Dr. Ramamurthy, a professor at the University of Nebraska-Lincoln’s School of Computing, with Dr. Derek Weitzel, a Research Assistant Professor at the HCC and UNL, serving as Co-PI. The project is an important contribution to improving the data management capabilities of HEP experiments, enhancing both infrastructure performance and scientific productivity.

CC* Integration-Small: Network Cyberinfrastructure Innovation with an Intelligent Real-Time Traffic Analysis Framework and Application-Aware Networking

Wed, 19 Jul 2023 00:00:00 +0000

Award Number: 2322369
Funding Agency: National Science Foundation (NSF)
Award Amount: $500,000
Project Duration: October 1, 2023 - September 30, 2025

Dr. Byrav Ramamurthy has secured a significant grant from the National Science Foundation (NSF) for his project, “Network Cyberinfrastructure Innovation with an Intelligent Real-Time Traffic Analysis Framework and Application-Aware Networking.”

This groundbreaking research seeks to transform network cyberinfrastructure by utilizing machine learning techniques for advanced traffic analysis and application-aware networking solutions.

The project focuses on developing a scalable framework for real-time network flow analysis, utilizing Just-In-Time (JIT) machine learning approaches to enhance decision-making capabilities within campus cyberinfrastructures. By integrating data from both campus networks and Internet2, the research aims to transform how network traffic is analyzed and managed.

Key objectives of the project include:

Real-Time Traffic Analysis: The development of online-offline machine learning approaches for real-time network traffic analysis and prediction, providing insights that surpass traditional offline methods.

Scalable Network Flow Analysis: Implementing theoretical models to transform, index, and build search techniques for analyzing network flow data at an internet-scale in real-time.

Application-Aware Networking: Utilizing software-defined networking (SDN) control strategies for application-aware data transfers, enabling greater flexibility in network management and service differentiation for scientific data transfers.

This research is supported by collaborations with the Holland Computing Center (HCC) at the University of Nebraska-Lincoln, the Open Science Grid Consortium (OSG), Argonne National Lab (ANL), and Internet2. The techniques and frameworks developed will be made available to the open-source community, benefiting various science applications within Research and Education (R&E) networks.

In addition to advancing the field of network cyberinfrastructure, the project is committed to enriching educational opportunities for students at the University of Nebraska-Lincoln’s School of Computing and conducting outreach events for the broader community.

This NSF award recognizes the project’s potential to make significant contributions to network cyberinfrastructure innovation, highlighting Dr. Ramamurthy’s leadership in advancing research with substantial intellectual merit and broader impacts.

Smart Grid Cybersecurity Enhancement Using Smart Authentication and Intelligent Threat Detection

Sun, 01 Jan 2023 00:00:00 +0000

Funding Agency: Nebraska Center for Energy Sciences Research (NCESR), in partnership with Nebraska Public Power District (NPPD)
Project Duration: January 2023 - December 2023 (Provisional: December 2024)

NCESR 2023: Security Measures For Smart Grid is a joint-venture between NetGroup (under Dr. Byrav Ramamurthy) and SWAN lab (under Dr. Nirnimesh Ghose). The aims of the project are to investigate, propose, design, and deploy security measures for smart grid security systems, particularly the NPPD (Nebraska Public Power District.) The main objectives of the project are below.

Abstract

This project proposes to address Smart Grid cybersecurity by developing innovative Artificial Intelligence (AI) and Machine Learning (ML) based solutions for (a) smart authentication of customer premises equipment such as smart meters and (b) real-time traffic analysis to detect anomalous behavior leveraging network- and application-layer collaboration as well as distributed threat intelligence frameworks. The goals of this project are to explore, evaluate and implement smart authentication and traffic analysis techniques to strengthen the security of the Smart Grid cyberinfrastructures. The project’s claim is that smart authentication of customer equipment by the Smart Grid network and continuous traffic analysis for anomalous behavior detection will ensure secure network operation and reduced threat landscape. The project team will develop smart device fingerprinting solutions that can ensure that unauthorized devices cannot become part of the Smart Grid cyberinfrastructure. Their approach will further demonstrate how application- and network-layer collaboration can be exploited to utilize application metadata for influencing secure control and management of the network. They will explore and integrate predictive analytics to provide intelligence for network resource management, improving network visibility for the operators, and securing the Smart Grid cyberinfrastructure. Apart from their research contributions in this project, the team will incorporate innovative modules on Smart Grid security in their undergraduate and graduate cybersecurity courses. During this project, they will partner with colleagues at Nebraska Public Power District (NPPD) and other public energy utilities in Nebraska to facilitate two-way sharing of data and findings to make a significant real-world impact. They will pursue external funding from federal agencies such as NSF, DHS and DOE to sustain and strengthen their research well into the future.

Outputs

“Hardware Isolated Smart Grid Security” at Spring Research Days 2023 Poster Session.

Intelligent Optical Networks Using Virtualization and Software-Defined Control

Wed, 29 Aug 2018 00:00:00 +0000

Award Number: 1817105
Funding Agency: National Science Foundation (NSF)
Award Amount: $665,995
Project Duration: October 1, 2018 - July 31, 2024

Dr. Byrav Ramamurthy, a distinguished faculty member of the University of Nebraska-Lincoln, has been awarded a significant grant from the National Science Foundation to lead a groundbreaking research project in intelligent optical networks.

This project, titled “Intelligent Optical Networks Using Virtualization and Software-Defined Control,” aims to redefine the capabilities of Internet Service Providers (ISPs) by implementing innovative solutions in software-defined networking (SDN) and virtualization.

The primary objective of this research is to develop a unified architecture for Software-Defined Optical Networks (SDON) that facilitates seamless end-to-end provisioning of services across heterogeneous, multi-domain networks. By addressing the complex challenges faced by large ISPs, this project seeks to enable:

Efficient End-to-End Provisioning: Implementation of fast, cost-effective, and integrated services over diverse network domains.

Dynamic Resource Allocation: Utilization of virtual transport links (VTL) and bandwidth-on-demand (BoD) techniques to optimize resource management.

Multi-Layer Network Optimization: Exploration of Resource Delayed Release (RDR) strategies and inter-domain tunnels to accelerate data transfers across Optical Transport Network (OTN), Wavelength Division Multiplexing (WDM), and Elastic Optical Networking (EON) layers.

The project’s innovative approach leverages SDN to overcome the challenges of modern ISP infrastructures, enhancing network automation and reducing costs. By integrating virtualization and software-defined control, Dr. Ramamurthy’s research is poised to usher in a new era of intelligent, adaptable, and efficient optical networks.

This prestigious NSF award underscores the project’s potential to contribute significantly to the field of networking technology and reflects the Foundation’s commitment to advancing research with substantial intellectual merit and broader impacts.