Dr. Khizar Abbas (Ph.D., Computer Engineering, Member IEEE)

Dr. Khizar Abbas

Designation: Assistant Professor (Research)

Institute: Department of Computer Science, Hanyang University, Seoul, South Korea.

Contact: engr.khizarabbas14@gmail.com, khizarabbas@hanyang.ac.kr

Dr. Khizar Abbas (Ph.D., Computer Engineering, Member IEEE) received his B.S. degree in software engineering from the Government College University Faisalabad (GCUF), Pakistan, in 2014 and an M.S. degree in computer science from the University of Agriculture Faisalabad, Pakistan, in 2017. He received a Ph.D. in computer engineering from Jeju National University, South Korea, in 2022. He is an Assistant Professor (Research) in the Computer Science Department of Hanyang University, Seoul, South Korea. Before this, He was a postdoctoral researcher with the Distributed Processing and Network Management (DPNM) Laboratory, Department of Computer Science and Engineering, POSTECH, South Korea. Previously, He worked as a computer science lecturer at the University College of Management and Science (UCMS), Khanewal, Pakistan. He also worked as a visiting lecturer for the Department of Computer Science, Government College University Faisalabad, Pakistan.

Dr. Khizar Abbas has three years of teaching experience to Computer Science/Engineering undergraduate students. Besides teaching, Dr. Khizar is actively performing research and has published more than 14 SCIE peer-reviewed papers in IEEE Transactions on Consumer Electronics, IEEE OJCOMS, ELSEVIER Physical Communication, IEEE Access, Transactions on Emerging Telecommunications Technologies, Wiley, Security and Communication Networks, Hindawi, Electronics, CMC, etc. Dr. Khizar has published over 20 articles in top-ranked network automation and softwarization conferences, including IEEE NOMS, IEEE APNOMS, IEEE ICOIN, IEEE VTC, and ACM Symposium on Applied Computing.

Research Interest

Software-Defined Networks, 5G, 6G, Network Slicing, Network Function Virtualization, Cloud Computing, Mobile Edge Computing, Multi-Access Edge Computing, Vehicular Networks
Network Automation and Orchestration, Network Security, Blockchain, Artificial Intelligence and Machine Learning for Network Intelligence and Automation, and Reinforcement Learning.

Research Projects

LTE-WiFi Spectrum Aggregation (LWA)

WiFi offloading is the best option to reduce the burden of cellular networks. So, aggregating indoor WiFi technology to the cellular network increases the network capacity and provides better QoS to customers. In this project, we have implemented the LTE-WiFi aggregation (LWA) system, where eNodeB aggregates the WiFi access point without modifying the core network. Furthermore, the LWA system is integrated with the mobile-CORD (M-CORD) platform, which leverages software-defined networking (SDN), network function virtualization (NFV), and cloud technologies for providing a 5G environment.

Network Slice LCM through Intent-based Networking

This project proposes an efficient solution that automates the configuration process and performs the management and orchestration of network slices. This solution contains a one-touch Intent-based Networking (IBN) platform that effectively orchestrates and manages the lifecycle of multi-domain slice resources. IBN automates the process of slice configuration generation, service provisioning, service update, and service assurance by eliminating experts and manual effort. Furthermore, it has an intelligent Deep Learning (DL) based resource update and assurance mechanism, which handles the run-time resource scalability and assurance.

Network Data Analytics for 5G Slicing

The third generation partnership project (3GPP) introduced artificial intelligence (AI) based network data analytics function (NWDAF) in 5G architecture for proactive management and control of network resources. NWDAF allows NOs to develop machine learning (ML) algorithms to manage networks intelligently. Therefore, we have proposed an Intent-based Networking (IBN) mechanism for automating and managing e2e network slicing and an AI-driven network data analytics mechanism for proactive and intelligent resource update and assurance. The network data analytics mechanism uses a hybrid stacking ensemble learning (STEL) algorithm to predict short-time network resource utilization and a novel Automated Machine Learning (autoML) and voting ensemble learning-based mechanism to detect and mitigate network anomalies.

Autonomous VM Consolidation in Cloud Data Centers

This work proposes an energy-efficient autonomous VM consolidation mechanism that has Deep Reinforcement Learning (DRL) based agent for performing VM consolidation decisions. The DRL agent learns the optimal distribution of VMs in the data center, considering energy efficiency and QoS assurance.

AI-based VNF resource Forecasting

This work introduces an efficient mechanism that uses Adaptive Ensemble Learning to predict resource usage of virtual network functions. This mechanism has three modules: Machine-Learning Predictors (MLPs), Predictor Selectors (PS), and Predictor Combiner (PC). The MLPs module contains several ML models for performing prediction. The PS module has a trained Random Forest model that chooses the best predictors from the MLPs. The PC module combines the selected predictors using an ensemble learning mechanism to generate the final prediction.

Professional Activities

Guest editor, special issue in Electronics, Impact Factor (2.68), Journal Rank Q2, SCIE: Emerging Topics in Industrial IoT, Networks, and Machine Learning
Session Chair: "IEEE 5th International Conference on AI in Information and Communication (ICAIIC 2023).
Reviewer Panel (SCIE, Peer Reviewed Journals):

IEEE Transactions on Network Services and Management.
IEEE Internet of Things
IEEE Access
Computer Networks (Elsevier)
International Journal of Network Management (Wiley)
Physical Communication (Elsevier)
Electronics (MDPI)
Applied Sciences (MDPI)
Computers, Materials & Continua (CMC)
Sensors (MDPI)
Computer Systems Science and Engineering (CSSE)
Transactions on Emerging Telecommunications Technologies (Wiley)
Computers and Electrical Engineering ((Elsevier)

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