Download or read book Adaptive Resource Allocation for Cloud Computing and Federated Learning written by 杜冰倩 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive Resource Management and Scheduling for Cloud Computing written by Florin Pop and published by Springer. This book was released on 2016-01-07 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2015, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2015, in Donostia-San Sebastián, Spain, in July 2015. The 12 revised full papers, including 1 invited paper, were carefully reviewed and selected from 24 submissions. The papers have identified several important aspects of the problem addressed by ARMS-CC: self-* and autonomous cloud systems, cloud quality management and service level agreement (SLA), scalable computing, mobile cloud computing, cloud computing techniques for big data, high performance cloud computing, resource management in big data platforms, scheduling algorithms for big data processing, cloud composition, federation, bridging, and bursting, cloud resource virtualization and composition, load-balancing and co-allocation, fault tolerance, reliability, and availability of cloud systems.
Download or read book Adaptive Resource Management and Scheduling for Cloud Computing written by Florin Pop and published by Springer. This book was released on 2014-11-25 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Download or read book Adaptive Resource Allocation in High performance Distributed Multimedia Computing written by Ran Yang and published by . This book was released on 2011 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On Adaptive Resource Allocation for Complex Real time Applications written by Daniela Roșu and published by . This book was released on 1997 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Resource Allocation in Embedded High Performance and Cloud Computing written by Leando Soares Indrusiak and published by CRC Press. This book was released on 2022-09-01 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.
Download or read book Adaptive Resource Allocation Algorithms for Data and Energy Integrated Networks Supporting Internet of Things written by Yitian Zhang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Frontier Computing written by Jason C. Hung and published by Springer Nature. This book was released on 2023-05-24 with total page 2016 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 12th International Conference on Frontier Computing, held in Tokyo, Japan, on July 12–15, 2022, and provides comprehensive coverage of the latest advances and trends in information technology, science, and engineering. It addresses a number of broad themes, including communication networks, business intelligence and knowledge management, Web intelligence, and related fields that inspire the development of information technology. The respective contributions cover a wide range of topics: database and data mining, networking and communications, Web and Internet of things, embedded systems, soft computing, social network analysis, security and privacy, optical communication, and ubiquitous/pervasive computing. Many of the papers outline promising future research directions, and the book benefits students, researchers, and professionals alike. Further, it offers a useful reference guide for newcomers to the field.
Download or read book IoT Edge Intelligence written by Souvik Pal and published by Springer Nature. This book was released on with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Resource Management for Time critical Tasks in the Cloud edge Continuum written by Hongyun Liu and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "As an emerging distributed computing paradigm, the Cloud-edge continuum (CEC) leverages the strengths of both cloud computing and edge computing to provide efficient and effective services to end-users. CEC enables faster processing of data and provides multiple benefits, including scalability, data security, and improved quality of service. With the increasing demand for real-time data processing, the proliferation of the Internet of Things (IoT) devices, and the growing need for data privacy and security, CEC has been developing, evolving, and adapting quickly. Cloud computing provides scalable and flexible computing infrastructure, while edge computing offers low latency and location-awareness capabilities.How to schedule the tasks in a CEC among its exploding amount of resources is a challenge for both service providers and users. QoS (quality of service) or QoE (Quality of experience) are metrics that describe this process and are often adopted as the optimization objective. Among all kinds of resource management optimization approaches, learning-based task scheduling and offloading have gained popularity in recent years. To overcome these limitations, researchers have turned to machine learning techniques to develop more intelligent and adaptive resource management algorithms. However, the machine learning-based methods in CEC also face several challenges:1. The performance of learning-based resource management is difficult to maintain when the pattern of time-critical tasks is dynamically changing;2. Learning-based resource management strategies are difficult to adapt when continuum resources are highly heterogeneous;3. Learning-based resource management suffers from low robustness when optimizing multiple objectives.My thesis tackles these challenges, and we propose a Meta-Learning-based resource management framework to deal with time-critical requests spanning from independent tasks to complex workflows in a dynamic cloud-edge continuum. Our goal is to improve the robustness and adaptivity of the resource management framework in highly changing environments."--
Download or read book Adaptive Resource Management and Scheduling for Cloud Computing Second International Workshop ARMS CC 2015 Held in Conjunction with ACM Symposium on Principles of Distributed Computing PODC 2015 Donostia San Sebasti n Spain July 20 2015 Revised Sel written by Florin Pop and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Resource Allocation for Adaptive Real time Applications written by Daniella Ivan-Roșu and published by . This book was released on 1999 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Federated Learning and AI for Healthcare 5 0 written by Hassan, Ahdi and published by IGI Global. This book was released on 2023-12-18 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Healthcare sector is evolving with Healthcare 5.0, promising better patient care and efficiency. However, challenges like data security and analysis arise due to increased digitization. Federated Learning and AI for Healthcare 5.0 offers solutions, explaining cloud computing's role in managing data and advocating for security measures. It explores federated learning's use in maintaining data privacy during analysis, presenting practical cases for implementation. The book also addresses emerging tech like quantum computing and blockchain-based services, envisioning an innovative Healthcare 5.0. It empowers healthcare professionals, IT experts, and data scientists to leverage these technologies for improved patient care and system efficiency, making Healthcare 5.0 secure and patient centric.
Download or read book Machine Learning Approach for Cloud Data Analytics in IoT written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-07-14 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
Download or read book Federated and Transfer Learning written by Roozbeh Razavi-Far and published by Springer Nature. This book was released on 2022-09-30 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Download or read book Wireless Algorithms Systems and Applications written by Lei Wang and published by Springer Nature. This book was released on 2022-11-17 with total page 679 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set constitutes the proceedings of the 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022, which was held during October 28-30, 2022. The conference took place in Dalian, China.The 95 full and 62 short papers presented in these proceedings were carefully reviewed and selected from 265 submissions. The contributions in theoretical frameworks and analysis of fundamental cross-layer protocol and network design and performance issues; distributed and localized algorithm design and analysis; information and coding theory for wireless networks; localization; mobility models and mobile social networking; underwater and underground networks; vehicular networks; algorithms, systems, and applications of edge computing
