This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and meta-heuristic approaches for placement techniques.
This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and meta-heuristic approaches for placement techniques.
Dr. Madhusudhan H S is currently working as Associate Professor in the Department of computer science and Engineering at Vidyavardhaka College of Engineering, Mysuru, Karnataka, India. He has published scientific research publications in reputed International Journals and Conferences including SCI indexed and Scopus indexed Journals. His area of interest includes Cloud computing, Artificial Intelligence, Machine learning and Computer Networks. He holds doctoral degree from Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India. Dr. Punit Gupta is Post-Doctoral Researcher at University College Dublin, Ireland. He has completed his Ph.D. in Computer Science and Engineering from Jaypee University of Information Technology, Solan, India. He is Gold Medalist in M-tech from Jaypee Institute of Information Technology. He has research experience in Internet-of-Things, Cloud Computing, and Distributed algorithms and authored more than 80 research papers in reputed journals and international conferences. He is gest editor in "Recent patent in compyter science" journal and editorial manager of "Computer Standards & Interfaces" and "Journal of Network and Computer Applications". He is currently serving as a Member of Computer Society of India (CSI), Member of IEEE, Professional member of ACM. He has organized a Special session on Fault tolerant and Reliable computing in Cloud, ICIIP 2019, India. He has published 50+ articles and book chapters in peer reviewed journals and conferences of international repute. He has enthusiastically participated and acted as organizing committee member of numerous IEEE and other conferences. Dr. Pradeep Singh Rawat Joined the Department of Computer Science & Engineering as an Assistant Professor on 7 January 2010. He has completed his Ph.D. work in Computer Science & Engineering from Uttarakhand Technical University, Dehradun, India, in the year of 2021. He received his MTech in Information Security & Management from Uttarakhand Technical University, Dehradun, and BTech in Computer Science & Engineering from Kumaun University, Nainital. Pradeep's interests center upon Cloud Computing & its Application, Data Communication & Networking, Data Science Application in Cloud, and Soft Computing. He received the Research Excellence Award 2020-21 at DITU and is a Bronze medalist in his post-graduation (MTech) from Uttarakhand Technical University, Dehradun, India. He received Outstanding Teaching award in academic year 2022-23 on behalf of School of Computing, DITU. He has published seven papers in SCIE indexed Journal with the highest impact factor (8.7). Dr. Rawat has more than 12 years of experience in academics. He has published and presented several research papers in various international journals and conferences of repute. He is an active member of the Universal Association of Computer and Electronics Engineers and has published 8 Scopus indexed book chapters.
Inhaltsangabe
1. Introduction To Next Generation Optimization In Cloud Computing Services 2: Challenges And Open Issues In Cloud Computing Services 3. Resource Management In Cloud Using Nature Inspired Algorithm 4.Machine Learning approaches for effective energy efficient resource management strategies in cloud services 5. Efficient Virtual Machine Allocation Technique Based On Hybrid Approach 6. Optimizing resource allocation in the cloud using deep learning 7. Reliable Resource Optimization Model for Cloud using Adversarial Neural Network 8. Efficient Migration Technique for Load Balancing in Cloud 9. Cost optimization model for cloud using Machine learning and Artificial intelligencd 10. Scalable optimization algorithm for Cloud resource scaling 11. Fault aware optimization using Machine Learning and Artificial Intelligence 12. Tools and Open Source Platforms for Cloud Computing
1. Introduction To Next Generation Optimization In Cloud Computing Services 2: Challenges And Open Issues In Cloud Computing Services 3. Resource Management In Cloud Using Nature Inspired Algorithm 4.Machine Learning approaches for effective energy efficient resource management strategies in cloud services 5. Efficient Virtual Machine Allocation Technique Based On Hybrid Approach 6. Optimizing resource allocation in the cloud using deep learning 7. Reliable Resource Optimization Model for Cloud using Adversarial Neural Network 8. Efficient Migration Technique for Load Balancing in Cloud 9. Cost optimization model for cloud using Machine learning and Artificial intelligencd 10. Scalable optimization algorithm for Cloud resource scaling 11. Fault aware optimization using Machine Learning and Artificial Intelligence 12. Tools and Open Source Platforms for Cloud Computing
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