This book blends the principles of cloud computing theory and discussion of emerging technologies in cloud broker systems, enabling users to realise the potential of an integrated broker system for scientific applications and the Internet of Things (IoT). Due to dynamic situations in user demand and cloud resource status, scalability has become crucial in the execution of complex scientific applications. Therefore, data analysts and computer scientists must grasp workflow management issues in order to better understand the characteristics of cloud resources, allocate these resources more efficiently and make critical decisions intelligently. Thus, this book addresses these issues through discussion of some novel approaches and engineering issues in cloud broker systems and cloudlets for workflow scheduling. This book closes the gaps between cloud programmers and scientific applications designers, describing the fundamentals of cloud broker system technology and the state-of-the-art applications in implementation and performance evaluation. The books gives details of scheduling structures and processes, providing guidance and inspiration for users including cloud programmers, application designers and decision makers with involvement in cloud resource management.

This timely text/reference presents a comprehensive review of the workflow scheduling algorithms and approaches that are rapidly becoming essential for a range of software applications, due to their ability to efficiently leverage diverse and distributed cloud resources. Particular emphasis is placed on how workflow-based automation in software-defined cloud centers and hybrid IT systems can significantly enhance resource utilization and optimize energy efficiency. Topics and features: describes dynamic workflow and task scheduling techniques that work across multiple (on-premise and off-premise) clouds; presents simulation-based case studies, and details of real-time test bed-based implementations; offers analyses and comparisons of a broad selection of static and dynamic workflow algorithms; examines the considerations for the main parameters in projects limited by budget and time constraints; covers workflow management systems, workflow modeling and simulation techniques, and machine learning approaches for predictive workflow analytics. This must-read work provides invaluable practical insights from three subject matter experts in the cloud paradigm, which will empower IT practitioners and industry professionals in their daily assignments. Researchers and students interested in next-generation software-defined cloud environments will also greatly benefit from the material in the book.

This book constitutes the proceedings of the 20th International Conference on Service-Oriented Computing, ICSOC 2022, held in Seville, Spain, in November -December 2022. The 29 full, 15 short, and 4 vision papers included in this volume were carefully reviewed and selected from 221 submissions. They were organized in topical sections named: ​service modeling and mining; quality of service; microservices; service personalization, recommendation, and crowdsourcing; blockchain; IoT and green computing; services for cloud, edge, and fog computing; artificial intelligence and machine learning for service computing; vision papers.

This book gathers selected papers presented at 3rd International Conference on Communication and Computational Technologies (ICCCT 2021), jointly organized in virtual format by Rajasthan Institute of Engineering and Technology, Jaipur and Rajasthan Technical University Kota in association with Soft Computing Research Society, during 27–28 February 2021. The volume is a collection of state-of-the-art research work in the cutting-edge technologies related to communication and intelligent systems. The topics covered are algorithms and applications of intelligent systems, informatics and applications, and communication and control systems.

CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.

This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2014, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2014, in Paris, France, in July 2014. The 14 revised full papers (including 2 invited talks) were carefully reviewed and selected from 29 submissions and cover topics such as scheduling methods and algorithms, services and applications, fundamental models for resource management in the cloud.

The two-volume set LNAI 12468 and 12469 constitutes the proceedings of the 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, held in Mexico City, Mexico, in October 2020. The total of 77 papers presented in these two volumes was carefully reviewed and selected from 186 submissions. The contributions are organized in topical as follows: Part I: machine and deep learning, evolutionary and metaheuristic algorithms, and soft computing. Part II: natural language processing, image processing and pattern recognition, and intelligent applications and robotics.