サクセナ　ディーピカー

サクセナ　ディーピカー SAXENA Deepika

准教授

所属
コンピュータ理工学科/情報システム学部門
職位
准教授
E-Mail
deepika@u-aizu.ac.jp
Webサイト
https://scholar.google.com/citations?user=NajK_n8AAAAJ&hl=en

教育

担当科目 - 大学
Introduction to Software Engineering (ICTG)
C++ Programming (ICTG)
Advanced Software Engineering
Algorithms and Data Structures I
担当科目 - 大学院

研究

研究分野
計算機システム
情報セキュリティ
高性能計算
ソフトコンピューティング
略歴
Dr. Deepika Saxena is a researcher and academician in the domain of Computer Science and Engineering. Currently, she is working as an Associate Professor in the Division of Information Systems in the Department of Computer Science and Engineering, The University of Aizu, Japan. Dr. Saxena has received her Ph.D. degree in Computer Science from the National Institute of Technology, Kurukshetra, India; and Post Doc from the Department of Computer Science, Goethe University, Frankfurt, Germany. Her major research interests include Neural networks, Evolutionary algorithms, Scheduling, and Security in cloud computing, Internet traffic management, Resource management, and Quantum machine learning, DataLakes, Dynamic Caching Management. Some of her research findings are published in top-cited journals such as IEEE TSC, IEEE TCC, IEEE TNSM, IEEE TPDS, IEEE Systems Journal, IEEE Communication Letters, IEEE Networking Letters, Neurocomputing, and IET Letters. She was an invited research seminars presenter about her doctoral research work at distinguished international venues, including the QORE Seminar at Imperial College London; the University of Melbourne, Australia; National Sun Yat-Sen University, Taiwan.
She was a visiting researcher at CERN, Geneva, Switzerland during her Postdoctoral period.

Recently, her Ph.D. thesis is awarded for the Best Ph.D. Thesis Award 2023 by The European Federation of Simulation Societies in Europe, EUROSIM 2023. Dr. Saxena has more than 60 publications now which include top peer-reviewed journals, conferences, book chapters, and in these areas. She has research and academic experience working in India, Germany, and Japan. She is a Member of the Institution of Electrical and Electronics Engineers (IEEE) Japan and several IEEE societies. She is appointed as a Guest Editor in Elsevier’s Computers & Electrical Engineering Journal (Q1 Ranking, SCI journal with Impact Factor: 4.152). She is an active review board member of various Q1 and Q2 ranking journals belonging to Elsevier, Springer, IEEE, IET, Wiley, etc., and conferences.

In her doctoral research work, she has addressed critical issues in the cloud environment including high power consumption, resource wastage, inefficient resource allocation, frequent migration of computing instances, security threats, high communication cost, fault-tolerance, sustainability, etc. by proposing and implementing various Artificial Intelligence-based Cloud Resource Prediction and Management models/frameworks/approaches. Specifically, her research work includes Evolutionary Quantum Neural Network (EQNN) and Quantum Blackhole learning-based Hadamard Neural Network (QB-HNN) models which are an intelligent collaboration of computational efficiency of Quantum mechanics and adaptive machine learning capabilities of evolutionary neural networks toward the prediction of a dynamic and extensive range of cloud workloads. An Online Predictive and Multi-objective Load Balancing framework incorporating VM prediction with scaling, resource distribution, on an allocation with VM migration at a unified platform, and allowing interaction among all the intended operations to optimize and tune together for overall performance improvement of cloud services, and Traffic Entropy Learning-based Load Management (TEL-LM) model is proposed which minimizes the effects of losses of inefficient VM allocation that occur due to load prediction errors. Online Secure inter-VM Communication (OSC-MC) and Security Embedded Dynamic Resource Allocation (SEDRA) models are developed for the secure execution of sensitive workloads in a shared computing environment by identifying and terminating malicious VMs and inter-VM links before the occurrence of security threats while minimizing the occurrence of traffic congestion-based attacks. Further, a secure and multi- objective optimization-based VM Placement (SM-VMP) framework is proposed to cater to the perspectives of both cloud users and service providers in conjunction. VM Significance Ranking and Resource Estimation for High Availability Management (SRE-HM) and Fault Tolerance based Elastic Resource Management (FT-ERM) frameworks are proposed for improving the availability of cloud services. Sustainable and Secure Load Management (SaS-LM) and highly Available, Secure, and Sustainable cloud Resource Management models are established to enhance security for users with improved sustainability for data centers. A novel AI-driven VM Threat Prediction Model is developed for Multi-Risks Analysis based Cloud Cybersecurity that identified the VMs threat before its occurrence.
現在の研究課題
研究内容キーワード
所属学会

主な著書?論文

1) Deepika Saxena, Ishu Gupta, Rishabh Gupta, Ashutosh Kumar Singh, and Xiaoqing Wen, “An AI-Driven VM Threat Prediction Model for Multi-Risks Analysis-Based Cloud Cybersecurity” IEEE Transactions on Systems, Man, and Cybernetics: Systems (SCI IF=11.471, Q1). DOI: 10.1109/TSMC.2023.3288081 (Accepted on 12 June 2023)

2) Deepika Saxena, Jitendra Kumar, Ashutosh Kumar Singh, and Stefan Schmid, “Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud” IEEE Transactions on Parallel and Distributed Computing, 2023 (SCI IF=3.757, Q1). DOI: 10.1109/TPDS.2023.3240567

3) Deepika Saxena, Ashutosh Kumar Singh, Chung-Nan Lee, Rajkumar Buyya,’’A Sustainable and Secure Load Management Model for Green Cloud Data Centre Networks”, Nature Scientific Reports, 2023 (SCI IF=4.379, Q1). DOI: 10.1038/s41598-023-27703-3

4) Smruti Swain, Deepika Saxena, Jatinder Kumar, Ashutosh Kumar Singh, and C. -N. Lee, "An AI-driven Intelligent Traffic Management Model for 6G Cloud Radio Access Networks", in IEEE Wireless Communications Letters, 2023 DOI: 10.1109/LWC.2023.3259942.

5) Ishu Gupta, Deepika Saxena, Ashutosh Kumar Singh, and Chung -Nan Lee "SeCoM: An Outsourced Cloud based Secure Communication Model for Advanced Privacy Preserving Data Computing and Protection", IEEE Systems Journal, 2023 (Accepted), DOI: 10.1109/JSYST.2023.3272611

6) Jatinder Kumar, Rishabh Gupta, Deepika Saxena, Ashutosh Kumar Singh “Power consumption forecast model using ensemble learning for smart grid”, The Journal of Supercomputing, 2023

7) Ashutosh Kumar Singh, Smruti Swain, Deepika Saxena, and C. -N. Lee, "A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management," in IEEE Systems Journal, 2023


8) Deepika Saxena, Ashutosh Kumar Singh, "A High Availability Management Model based on VM Significance Ranking and Resource Estimation for Cloud Applications", IEEE Transactions on Services Computing (SCI IF=11.019, Q1).

9) Deepika Saxena, Ashutosh Kumar Singh, and Rajkumar Buyya, "OP-MLB: An online VM prediction based multi-objective load balancing framework for resource management at cloud datacenter", IEEE Transactions on Cloud Computing, 2021 (SCI IF=5.697, Q1).

10) Deepika Saxena, Ishu Gupta, Ashutosh Kumar Singh, and Chung -Nan Lee "A Fault-Tolerant Elastic Resource Management Framework for High Availability of Cloud Services", IEEE Transactions on Network and Service Management, 2022 (SCI IF=4.758, Q1)

11) Deepika Saxena, Ishu Gupta, Jitendra Kumar, Ashutosh Kumar Singh, and Xiaoqing Wen, "A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center", IEEE Systems Journal, 2021 (SCI IF=4.802, Q1).

12) Ashutosh Kumar Singh, Deepika Saxena, Jitendra Kumar, and Vrinda Gupta, "A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads", IEEE Transactions on Parallel and Distributed Systems, 2021 (SCI IF=3.757, Q1).

13) Deepika Saxena, and Ashutosh Kumar Singh, "OSC-MC: Online Secure Communication Model for Cloud Environment", IEEE Communications Letters, 2021 (SCI IF=3.436, Q1).

14) Deepika Saxena, Ashutosh Kumar Singh, "An Intelligent Traffic Entropy Learning based Load Management Model for Cloud Networks", IEEE Networking Letters, 2022 (SCI IF=3.436, Q1)

15) Rishabh Gupta, Deepika Saxena, and Ashutosh Kumar Singh, "Differential and Tri-Phase adaptive learning-based Privacy-Preserving Model for Medical Data in Cloud Environment", IEEE Networking Letters, 2022 (SCI IF=3.436, Q1).

16) Rishabh Gupta, Deepika Saxena, Ishu Gupta, Ayesha Makkar, and Ashutosh Kumar Singh, "Quantum Machine Learning-Driven Malicious User Prediction Model for Secure Cloud Communications", IEEE Networking Letters, 2022 (SCI IF=3.436, Q1).

17) Rishabh Gupta, Ishu Gupta, Ashutosh Kumar Singh, Deepika Saxena, and Chung-Nan Lee, "An IoT-Centric Data Protection Method for Preserving Security and Privacy in Cloud", IEEE Systems Journal (SCI IF=4.802, Q1)

18) Deepika Saxena, and Ashutosh Kumar Singh, "A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center", Neurocomputing 426, 2021, 248-264 (SCI IF=5.719, Q1).

19) Jitendra Kumar, Deepika Saxena, Ashutosh Kumar Singh, and Anand Mohan, "Biphase adaptive learning-based neural net