My team's research primarily involves developing architecture, algorithm, and experimental testbed for optically-switched quantum and data center networking. We also work on resource allocation and the application of machine learning in communication networks. Here is a summary of my team's research areas.
Quantum Networking
Classical Internet employs buffering, switching, and routing functionality to send information over thousands of kilometers of under-sea fiber-optical cables, while exposing the data to attackers. Quantum networks provide an alternative paradigm where data can be encoded into photonic degrees of freedom, e. g., polarization, and transmitted as entangled (correlated) photons. This secures quantum bits (qubits) from attackers due to their properties, such as qubits not being amplified, duplicated, or measured without altering them. However, future quantum networks need to imitate the functionality of the classical Internet, which is challenging. We are developing a quantum networks, which can transmit qubit payload
Datacenter Networking and High Performance Computing Systems
Today's datacenter (DC) and high performance computing (HPC) systems need to support reconfigurable data plane (hardware) and control/management plane (software) solutions leveraging the unique benefits of optical interconnect technologies and machine-learning-aided network optimization techniques. We work towards designing a novel agile HPC system with low-diameter and application-driven elastic optical bandwidth assignment· The goal is to provide a low-latency communication network that can serve today’s and tomorrow’s data-intensive applications that require fast and efficient data movement tailored to the applications’ communication profile.
Network Security
The communication systems need to improve the integration of various data sources and services into a networked system for the detection and management of security scenarios in real time. The aim is to scientifically investigate which AI/ML methods provide best possible results in the field of the detection of anomalies.
Resource Allocation in Optical Networks
The efficient allocation of optical resources is challenging with the elastic optical networks (EONs), as setup and tear down of non-uniform bandwidth requests fragments spectrum in spectral, time and spatial dimensions. Therefore, it must be modeled accurately, and managed efficiently and intelligently. Theoretical models and algorithms are presented for resource allocation in EONs.We also utilize machine learning techniques for handling resources and traffic in optical datacenter networks, and managing bandwidth and energy consumption in network and edge devices in fiber-wireless networks..
Teaching
I teach the following courses in the Department of Electronics and Communication Engineering at IIT Roorkee. Please sign up for courses on the University website. If you have any questions about course topics, please contact me.
ECN-603: Terahertz Communication System, 2024 (Spring);
ECC-507: Essential Concepts in Terahertz Communication, 2024 (Autumn);
IPQ-301: A Primer in Quantum Technology, 2024 (Autumn);
ECN-391: Technical Communication, 2024 (Autumn);
ECN 502: Terahertz Communication and Sensing lab-1 (2023) and lab-2 (2024);