Scheduling Tasks in Distributed Cloud and Edge Computing Systems with Evolutionary Optimizers
Description
This book focuses on the challenges and solutions for scheduling tasks in distributed cloud and edge computing systems, with a particular emphasis on predicting workload and resources and optimizing performance and resource utilization through innovative algorithms and methodologies. The book provides an in-depth exploration of theoretical and practical aspects across seven comprehensive parts. The book first introduces the key concepts of cloud computing, edge computing, and their convergence in distributed cloud-edge systems. The authors then lay the groundwork for understanding workload prediction, energy management, and integrating cloud-edge infrastructures with large artificial intelligence (AI) models. The book then presents a detailed examination of workload and resource prediction techniques. Next, task scheduling is explored with a focus on energy efficiency and performance in unmanned aerial vehicles (UAVs), satellite-terrestrial edge networks, etc. The book also delves into integrating large-scale AI models within cloud-edge systems and introduces innovative practices of new infrastructure in cloud-edge systems. Finally, real-world applications of distributed cloud-edge systems are discussed across various domains. This book provides valuable resources for researchers, engineers, and professionals seeking to advance their knowledge of distributed cloud and edge computing systems and their applications in emerging areas.
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Persons
Haitao Yuan, IEEE Senior Member, received the Ph.D. degree in Computer Engineering from New Jersey Institute of Technology (NJIT), Newark, NJ, USA in 2020. He is currently a Deputy Director in the Department of Strategic Development, Wenchang International Aerospace City Administration, Hainan, China. He is currently an Associate Professor at the School of Automation Science and Electrical Engineering at Beihang University, Beijing, China, and he is named in the world's top 2% of Scientists List since 2022. His research interests include the Internet of Things, edge intelligence, deep learning, data-driven optimization, and computational intelligence algorithms. He received the Chinese Government Award for Outstanding Self-Financed Students Abroad, the 2021 Hashimoto Prize from NJIT, the Best Work Award in the 17th International Conference on Networking, Sensing and Control, and the Best Student Work Award Nominee in the 2024 IEEE International Conference on Systems, Man, and Cybernetics. He is an associate editor for IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Internet of Things Journal, and Expert Systems With Applications.
Jing Bi (Senior Member, IEEE) received her B.S., and Ph.D. degrees in Computer Science from Northeastern University, Shenyang, China, in 2003 and 2011, respectively. She is currently a Professor with the Faculty of Information Technology, Beijing University of Technology, Beijing, China. She has over 170 publications in international journals and conference proceedings. Her research interests include distributed computing, cloud & edge computing, large-scale data analytics, machine learning, industrial internet, and performance optimization. She is now an Associate Editor of IEEE Transactions on Systems Man and Cybernetics: Systems. She is a senior member of the IEEE.
MengChu Zhou (Fellow, IEEE) received the B.S. degree in control engineering from Nanjing University of Science and Technology, Nanjing, China, in 1983, the M.S. degree in automatic control from Beijing Institute of Technology, Beijing, China, in 1986, and the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, NY, USA, in 1990.,He joined the Department of Electrical and Computer Engineering, New Jersey Institute of Technology in 1990, and is now a Distinguished Professor. His interests are in intelligent automation/transportation, robotics, Petri nets, Internet of Things, edge/cloud computing, and big data analytics. He has over 1300 publications including 17 books.
Rajkumar Buyya (Fellow, IEEE) is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He received a B.E and M.E in Computer Science and Engineering from Mysore and Bangalore Universities in 1992 and 1995, respectively, and a Ph.D. in Computer Science and Software Engineering from Monash University, Melbourne, Australia, in 2002. He was a Future Fellow of the Australian Research Council from 2012 to 2016. He has authored over 800 publications and seven textbooks. He is one of the highly cited authors in computer science and software engineering worldwide, with over 166,712 citations and an h-index of 176. He was recognized as a ``Web of Science Highly Cited Researcher" from 2016 to 2021 by Thomson Reuters.
Content
Introduction.- Part I. DISTRIBUTED CLOUD AND EDGE COMPUTING SYSTEMS.- Preliminaries.- Part II. SINGLE-TASK AND MULTI-TASK PREDICTION.- Multiapplication Workload Prediction with Wavelet Decomposition.- Network Traffic Prediction with Temporal Convolutional Networks and LSTM.- Multivariate Resource Usage Prediction with Frequency-Enhanced and Attention-Assisted Transformer.- LSTM-Based Prediction for Large-Scale Resources and Workloads.- Workload and Resource Prediction with Multi-Head Attention and LSTM.- Spatio-temporal Prediction with Bi-directional and Grid LSTM for Workloads and Resources in Clouds.- Part III. TASK SCHEDULING STRATEGIES.- Energy-Efficient Offloading for Static and Dynamic Applications.- Cost-Minimized Offloading and User Association.- Energy-Optimized Partial Computation Offloading.- Cost-Minimized Microservice Migration with Autoencoder-Assisted Evolution.- Cost-Efficient Offloading with Layered Unmanned Aerial Vehicles.- Energy-Minimized Partial Offloading in Satellite-Terrestrial Edge Networks.- Part IV. CLOUD AND EDGE SYSTEMS FOR LARGE AI MODELS.- Multimodal Large Models and Their Applications.- Large Prediction Models and Their Applications.- Inference Offloading and Resource Allocation with Large Models.- Part V. INNOVATIVE PRACTICES IN CLOUD-EDGE SYSTEMS.- Applications of Cloud-Edge Systems in CDNs.- Applications of Cloud-Edge Systems in Industrial Internet.- Applications of Cloud-Edge Systems in Energy Internet.- Applications of Cloud-Edge Systems in Smart Buildings.- Applications of Cloud-Edge Systems in Smart Transportation.- Part VI. INNOVATIVE PRACTICES IN CLOUD-EDGE SYSTEMS: INDUSTRIAL APPLICATIONS.- Applications of Cloud-Edge Systems in Security Monitoring.- Applications of Cloud-Edge Systems in Agricultural Production.- Applications of Cloud-Edge Systems in Ecological Environment.- Applications of Cloud-Edge Systems in Healthcare.- Applications of Cloud-Edge Systems in Smart Education.- Part VII. CONCLUSIONS AND OPEN PROBLEMS.- Conclusion.