Prof. Ashutosh Kumar Singh
Indian Institute of Information Technology Bhopal, India
Prof. Ashutosh Kumar Singh joined IIIT Bhopal as Founder Director in May 2023. He is a distinguished scholar and educator specializing in Electronics and Computer Engineering. He is charted engineer and earned his Ph.D. in Electronics Engineering from the prestigious Indian Institute of Technology-BHU, Varanasi, India. He has been a Post-Doctoral Research Fellow with the Department of Computer Science, University of Bristol, United Kingdom. He received the prestigious Japan Society for the Promotion of Science (JSPS) fellowship to visit the University of Tokyo and other Japanese Universities as a visiting researcher. He has also worked as a visiting scientist at the Frankfurt Institute for Advanced Studies, Goethe University, Germany.
Prof. Singh’s research focuses on a broad range of topics such as Cloud Computing, Big Data Analytics, Machine Learning, Cyber Security, Verification, Synthesis, Design and Testing of Digital Circuits, and Web Technology. He has guided 11 doctoral theses and more than 100 master’s students. He has authored over 350 publications in various forms such as peer-reviewed journals, books, conferences, book chapters, and news magazines. He has co-authored eleven books, including “Machine Learning for Cloud Management”, “Web Spam Detection Application using Neural Network,” “Digital Systems Fundamentals,” and “Computer System Organization & Architecture.” He has also served as the principal investigator/investigator for six sponsored research projects and played a vital role in a project funded by EPSRC (United Kingdom) titled “Logic Verification and Synthesis in New Framework.”
Prof. Singh has traveled extensively across several countries, including Australia, the United Kingdom, South Korea, China, Thailand, Indonesia, Japan, Germany, Switzerland, and the USA, for collaborative research work, invited talks, and presented his research findings. He has received multiple awards, including Best Paper Awards, Merit Awards-2003 (Institute of Engineers), Best Presenter Awards, and the Bintulu Development Authority Best Postgraduate Research Paper Award. Prof. Singh continuously serves many journals and conferences in different capacities such as associate editor, chair, advisory committee member, etc.
Fault-Tolerant, Availability-Aware, and Quantum Predictive Intelligence Models for Cloud Computing
Abstract: In the age of Industrial Cloud Computing, the exponential rise in resource demands introduces critical operational challenges ranging from service interruptions and performance degradation to elevated energy consumption and increased exposure to security threats. These problems are often exacerbated by rigid virtual machine (VM) configurations, unpredictable workload fluctuations, and the co-location of multiple tenants on shared infrastructure. Consequently, the need for intelligent, fault-tolerant, and energy-aware cloud resource management strategies has become more pressing than ever. This research presents two novel frameworks such as VM Significance Ranking and Resource Estimation-based High Availability Management (SRE-HM) and Fault-Tolerant Elastic Resource Management (FT-ERM) to address these challenges with an integrated focus on reliability, efficiency, and sustainability. The SRE-HM framework enhances cloud service availability by dynamically prioritizing critical VMs based on their significance and estimated resource needs. Empirical results demonstrate a 19.56% improvement in service availability, along with a 26.67% reduction in active server usage and a 19.1% decrease in power consumption, thereby improving both cost-effectiveness and operational resilience. In parallel, the FT-ERM model introduces a predictive, fault-aware elasticity mechanism that proactively monitors and responds to server anomalies. This approach significantly improves system responsiveness, achieving a 34.47% boost in service availability, an 88.6% reduction in VM migrations, and a 62.4% drop in overall power usage. Together, these frameworks provide a robust and scalable foundation for intelligent cloud operations. Furthermore, this talk introduces a forward-looking research direction featuring an Evolutionary Quantum Neural Network (EQNN) model, designed to forecast highly dynamic and diverse cloud workload patterns with superior accuracy and adaptability. This hybrid model integrates quantum computing principles with evolutionary learning mechanisms to enhance prediction reliability under real-time, large-scale cloud environments. Overall, this research paves the way for next-generation self-healing, secure, and energy- efficient industrial cloud infrastructures catalyzing advancements in automation, resource optimization, and cloud-native cybersecurity. Future work will focus on integrating federated learning and blockchain-based trust frameworks to further strengthen privacy-preserving, decentralized, and resilient cloud service ecosystems.
Assoc. Prof. Deepika Saxena
The University of Aizu, Japan
Dr. Deepika Saxena is a researcher and academician in the domain of Computer Science and Engineering. Since April 2023, she has been working as an Associate Professor at the Department of Computer Science and Engineering, University of Aizu, JAPAN. Also, she is working as an Adjunct Professor at the VIZJA University, Warsaw, Poland, Europe. Dr. Saxena received her Ph.D. in Computer Science from the National Institute of Technology, Kurukshetra, INDIA, and completed her Postdoctoral research at the Department of Computer Science, Goethe University, Frankfurt, GERMANY. Recently, she is awarded the prestigious Japan Society for the Promotion of Science (JSPS) KAKENHI Early Career Young Scientist Research Grant for FY2024 up to 2027. She is the recipient of the esteemed Poland Minister of Science Scholarships for Outstanding Young Scientists 2024. Her innovative Ph.D. research led to the successful acquisition of two patents in 2024. Also, she has received Competitive Research Grants at the University of Aizu, Japan in 2023, 2024, and 2025. Within three years of her Ph.D. completion, she has positioned herself among the World’s Top 2% Scientists List by Stanford University and Elsevier.
She has recently received the esteemed IEEE TCSC 2024 Early Career Researcher Award and IEEE TCSC 2023 Outstanding Ph.D. Dissertation Award, a recognition she has received at the IEEE HPCC 2023 Conference in Melbourne, Australia. Additionally, her outstanding Ph.D. thesis has been bestowed with the prestigious EUROSIM 2023 Best Ph.D. Thesis Award by The European Federation of Simulation Societies. These accolades emphasize her significant contributions to the academic community. Her paper, “OP-MLB: An Online VM Prediction-Based Multi-Objective Load Balancing Framework for Resource
Management at Cloud Data Center,” has been awarded the 2022 Best Paper Award from IEEE Transactions on Cloud Computing Journal by the IEEE Computer Society Publications Board.
Her research interests are extensive and include neural networks, evolutionary algorithms, scheduling, and security in cloud computing, internet traffic management, resource management, quantum machine learning, data lakes, and dynamic caching management. Dr. Saxena has made substantial contributions to these fields, with over 100+ scientific research publications in highly regarded and widely cited journals such as IEEE TDSC, IEEE TPAMI, IEEE TNNLS, IEEE TII, IEEE TSC, IEEE TSUSC, IEEE TGCN, IEEE TSMC, IEEE T-ASE, IEEE TCC, IEEE TNSM, IEEE TPDS, IEEE Systems Journal, IEEE Wireless Communication Letters, IEEE Communication Letters, IEEE Networking Letters, Neurocomputing, and IET Letters, etc. She served as a Visiting Researcher at CERN, Geneva, during her postdoctoral tenure.
Fault-Tolerant and Cybersecure Virtual Machine Management for Resilient Industrial Cloud Systems.
Abstract: Industrial Cloud Computing faces growing challenges such as service outages, energy inefficiency, and rising cybersecurity threats due to surging resource demands. To address these issues, this research talk will focus on four novel models categorized into fault-tolerance and security domains. For fault tolerance, the SRE-HM (Significance Ranking and Estimation-based High Availability Management) model ranks virtual machines (VMs) based on task criticality and proactively estimates resource requirements, enabling selective high availability strategies. This leads to a 19.56% improvement in service availability, a 26.67% reduction in active servers, and a 19.1% decrease in power consumption. The SF-DTM (Self-Healing and Fault-Tolerant Digital Twin Management) model enhances the reliability of Digital Twin (DT) processing by integrating SimiFed, a federated learning method with cosine similarity for collaborative resource estimation, alongside a fault-pattern analytics unit for resilient VM allocation. It improves service availability by 13.2%, increases MTBF, and reduces MTTR. For security, the MR-TPM (Multiple Risks-based Threat Prediction Model) predicts VM-level threats using multi-risk analytics, achieving up to 88.9% threat reduction. Complementing this, the ETP-WE (Emerging Threat Prediction and Workload Estimation) framework combines machine learning-driven threat prediction with workload-aware resource estimation, reducing vulnerabilities by 86.9%, power consumption by 66.67%, and active servers by 80%. Collectively, these models provide a robust, self-healing, energy-efficient, and cyber-resilient foundation for next-generation industrial cloud infrastructures.