Download or read book Resource aware Distributed Analytics and Machine Learning for Hybrid Edge cloud Systems written by Anirban Das and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Orchestrating a Resource aware Edge written by Klervie Toczé and published by Linköping University Electronic Press. This book was released on 2024-09-02 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: More and more services are moving to the cloud, attracted by the promise of unlimited resources that are accessible anytime, and are managed by someone else. However, hosting every type of service in large cloud datacenters is not possible or suitable, as some emerging applications have stringent latency or privacy requirements, while also handling huge amounts of data. Therefore, in recent years, a new paradigm has been proposed to address the needs of these applications: the edge computing paradigm. Resources provided at the edge (e.g., for computation and communication) are constrained, hence resource management is of crucial importance. The incoming load to the edge infrastructure varies both in time and space. Managing the edge infrastructure so that the appropriate resources are available at the required time and location is called orchestrating. This is especially challenging in case of sudden load spikes and when the orchestration impact itself has to be limited. This thesis enables edge computing orchestration with increased resource-awareness by contributing with methods, techniques, and concepts for edge resource management. First, it proposes methods to better understand the edge resource demand. Second, it provides solutions on the supply side for orchestrating edge resources with different characteristics in order to serve edge applications with satisfactory quality of service. Finally, the thesis includes a critical perspective on the paradigm, by considering sustainability challenges. To understand the demand patterns, the thesis presents a methodology for categorizing the large variety of use cases that are proposed in the literature as potential applications for edge computing. The thesis also proposes methods for characterizing and modeling applications, as well as for gathering traces from real applications and analyzing them. These different approaches are applied to a prototype from a typical edge application domain: Mixed Reality. The important insight here is that application descriptions or models that are not based on a real application may not be giving an accurate picture of the load. This can drive incorrect decisions about what should be done on the supply side and thus waste resources. Regarding resource supply, the thesis proposes two orchestration frameworks for managing edge resources and successfully dealing with load spikes while avoiding over-provisioning. The first one utilizes mobile edge devices while the second leverages the concept of spare devices. Then, focusing on the request placement part of orchestration, the thesis formalizes it in the case of applications structured as chains of functions (so-called microservices) as an instance of the Traveling Purchaser Problem and solves it using Integer Linear Programming. Two different energy metrics influencing request placement decisions are proposed and evaluated. Finally, the thesis explores further resource awareness. Sustainability challenges that should be highlighted more within edge computing are collected. Among those related to resource use, the strategy of sufficiency is promoted as a way forward. It involves aiming at only using the needed resources (no more, no less) with a goal of reducing resource usage. Different tools to adopt it are proposed and their use demonstrated through a case study.

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 Machine Learning and Optimization Models for Optimization in Cloud written by Punit Gupta and published by CRC Press. This book was released on 2022-02-27 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Download or read book Managing Distributed Cloud Applications and Infrastructure written by Theo Lynn and published by Springer Nature. This book was released on 2020-07-20 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities.

Download or read book Fog and Edge Computing written by Rajkumar Buyya and published by John Wiley & Sons. This book was released on 2019-01-04 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to Fog and Edge applications, architectures, and technologies Recent years have seen the explosive growth of the Internet of Things (IoT): the internet-connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever-increasing bandwidth demands of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data more efficiently than traditional cloud architecture. Fog and Edge Computing: Principles and Paradigms provides a comprehensive overview of the state-of-the-art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource: Provides insights on transitioning from current Cloud-centric and 4G/5G wireless environments to Fog Computing Examines methods to optimize virtualized, pooled, and shared resources Identifies potential technical challenges and offers suggestions for possible solutions Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management Includes access to a website portal for advanced online resources Fog and Edge Computing: Principles and Paradigms is an essential source of up-to-date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.

Download or read book Latency aware Resource Management at the Edge written by Klervie Toczé and published by Linköping University Electronic Press. This book was released on 2020-02-19 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing diversity of connected devices leads to new application domains being envisioned. Some of these need ultra low latency or have privacy requirements that cannot be satisfied by the current cloud. By bringing resources closer to the end user, the recent edge computing paradigm aims to enable such applications. One critical aspect to ensure the successful deployment of the edge computing paradigm is efficient resource management. Indeed, obtaining the needed resources is crucial for the applications using the edge, but the resource picture of this paradigm is complex. First, as opposed to the nearly infinite resources provided by the cloud, the edge devices have finite resources. Moreover, different resource types are required depending on the applications and the devices supplying those resources are very heterogeneous. This thesis studies several challenges towards enabling efficient resource management for edge computing. The thesis begins by a review of the state-of-the-art research focusing on resource management in the edge computing context. A taxonomy is proposed for providing an overview of the current research and identify areas in need of further work. One of the identified challenges is studying the resource supply organization in the case where a mix of mobile and stationary devices is used to provide the edge resources. The ORCH framework is proposed as a means to orchestrate this edge device mix. The evaluation performed in a simulator shows that this combination of devices enables higher quality of service for latency-critical tasks. Another area is understanding the resource demand side. The thesis presents a study of the workload of a killer application for edge computing: mixed reality. The MR-Leo prototype is designed and used as a vehicle to understand the end-to-end latency, the throughput, and the characteristics of the workload for this type of application. A method for modeling the workload of an application is devised and applied to MR-Leo in order to obtain a synthetic workload exhibiting the same characteristics, which can be used in further studies.

Download or read book Beyond Edge Computing written by Ana Juan Ferrer and published by Springer Nature. This book was released on 2023-03-22 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the most recent Edge and Distributed Cloud computing research and industrial advances, settling the basis for Advanced Swarm Computing developments. It features the Swarm computing concepts and realizes it as an Ad-hoc Edge Cloud architecture. Unlike current techniques in Edge and Cloud computing that solely view IoT connected devices as sources of data, Swarm computing aims at using the compute capabilities of IoT connected devices in coordination with current Edge and Cloud computing innovations. In addition to being more widely available, IoT-connected devices are also quickly becoming more sophisticated in terms of their ability to carry considerable compute and storage resources. Swarm computing and Ad-hoc Edge Cloud take full advantage of this trend to create on-demand, autonomic and decentralized self-managed computing infrastructures. Focusing on cognitive resource and service management, the book examines the specific research challenges of the Swarm computing approach, related to the characteristics of IoT connected devices that form the infrastructure. It also offers academics and practitioners insights for future research in the fields of Edge and Swarm computing.

Download or read book Architecting Data and Machine Learning Platforms written by Marco Tranquillin and published by "O'Reilly Media, Inc.". This book was released on 2023-10-12 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach

Download or read book Intelligent Industrial Internet Systems written by Wanchun Dou and published by Springer Nature. This book was released on 2023-11-20 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the process of transforming modern manufacturing into intelligent manufacturing, more and more manufacturing processes are being deployed digitally on the Internet. As a novel digital industrial infrastructure and new application ecology, Industrial Internet could integrate the novel information and communication technology into various industrial manufacturing processes, through the safe and reliable intelligent interconnection of factors concerning people, machines and things. It aims at significantly improving the digitalization, networking and intelligence of manufacturing processes through the comprehensive interconnection of all manufacturing factors. To deploy and run traditional manufacturing processes on Industrial Internet efficiently, corresponding digital technical means are called for. These technical means play an important role in many potential applications of intelligent manufacturing, e.g. integrating product lifecycle data, strengthening industrial chain cooperation, providing decision-making service information oriented on the production organization process, and providing data support for product optimization and upgrading. This book focuses on the theory, techniques and means for integrating intelligent Industrial Internet applications deployed via mobile edge computing. Concretely, it mainly studies the intelligent theory, techniques and means from a problem-driven and demand-driven perspective, which supports collaborative design, agile manufacturing, remote operation and maintenance on Industrial Internet. These technologies primarily include Industrial Internet-centric intelligent resource scheduling methods, various types of industrial data privacy protection, intelligent version management technologies supporting digital twin applications, various data mining algorithms for intelligent execution of business processes, etc. Given its scope, the book offers a valuable theoretical and technical resource for project managers, technical developers, researchers, educators and students at colleges and universities.

Download or read book Resource Management in Distributed Systems written by Anwesha Mukherjee and published by Springer Nature. This book was released on with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning for Edge Computing written by Amitoj Singh and published by CRC Press. This book was released on 2022-07-29 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence. The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needs Amitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India. Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India. Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.

Download or read book Fog Computing for Intelligent Cloud IoT Systems written by Chandan Banerjee and published by John Wiley & Sons. This book was released on 2024-06-04 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources. Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user’s experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service. Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing’s help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage. This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered. Audience The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems.

Download or read book Service Oriented Computing ICSOC 2017 Workshops written by Lars Braubach and published by Springer. This book was released on 2018-06-15 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the scientific satellite events that were held in conjunction with the 15th International Conference on Service-Oriented Computing, ICSOC 2017, held in Málaga, Spain, in November 2017. The ICSOC 2017 workshop track consisted of three workshops on a wide range of topics that fall into the general area of service computing: ASOCA 2017: The Second Workshop on Adaptive Service-Oriented and Cloud Applications ISyCC 2016: The Second Workshop on IoT Systems Provisioning and Management in Cloud Computing WESOACS 2017: The 13th International Workshop on Engineering Service-Oriented Applications and Cloud Services

Download or read book Edge Computing EDGE 2021 written by Liang-Jie Zhang and published by Springer Nature. This book was released on 2022-03-02 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the International Conference on Edge Computing, EDGE 2021, held virtually as part of SCF 2021, held as a Virtual Event, December 10–14, 2021. The 5 full and 2 short papers presented in this volume were carefully reviewed and selected from 18 submissions. The conference proceeding EDGE 2021 presents the latest fundamental advances in the state of the art and practice of edge computing, identify emerging research topics, and define the future of edge computing.

Download or read book Proceedings of the 13th International Conference on Computer Engineering and Networks written by Yonghong Zhang and published by Springer Nature. This book was released on 2024-01-03 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to examine innovation in the fields of computer engineering and networking. The text covers important developments in areas such as artificial intelligence, machine learning, information analysis, communication system, computer modeling, internet of things. This book presents papers from the 13th International Conference on Computer Engineering and Networks (CENet2023) held in Wuxi, China on November 3-5, 2023.

Download or read book Distributed Cloud written by Weiqi Tian and published by Independently Published. This book was released on 2023-11-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid and multi-cloud are already powerful, but they cannot solve some problems such as interoperability, portability, and inconsistency. Moreover, the central controller of the Distributed Cloud suggested by Gartner is bound to a cloud provider, thus cannot solve the problems of Vendor-Locking and operation/integration neutrality issues, etc. This book proposes a different architecture that takes a single central control point, OneCenter, residing anywhere. OneCenter is independent and not bound to any cloud provider. OneCenter manages geo-distributed infrastructures from any providers in a neutral, consistent, and unified approach globally. About the Book This book proposes a new creative architecture with four essential layers including infrastructure, abstraction, resource, and workload layer. First, this architecture elaborates a versatile OneCenter, depicts the concept of an elastic distributed site, and expands the scope of intelligent scheduling/provisioning of computing, networking, and storage resources to cloud, on-premises, and telco edge computing worldwide. Second, this book designs a new modern big data platform and a scalable distributed conference edge computing platform based on the Distributed Cloud architecture natively. Finally, this book also presents some new innovative points of cloud architecture such as the talent VDC and GVPC. Those are advantages and features that all conventional cloud platforms lack and are also the future development trend of cloud computing. About the Technology In the Distributed Cloud, OneCenter leverages SD-WAN, VXLAN, EVPN, and OVN's advanced routing, connection, stitching, extension, and multi-tenant capacities to implement the GVPC for inner and inter-site communication. Unified Resource Manager is used to manage and orchestrate the diverse heterogeneous underlying infrastructures to implement the unified Bare Metal, Virtual Machine, and Container workload across geo-distributed sites worldwide. OneStorage is implemented with an efficient multi-tier block storage system and integrates multiple S3-compatible globally available object storage systems to meet customers' compliance, costs, and business requirements. OneData greatly benefits from the modern data architecture with Hybrid Data Mesh and Data Fabric architecture. Besides, Telco edge computing is also involved to innovate and empower edge computing in the Distributed Cloud.