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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.
Disorder-assistive and neurotechnological devices are experiencing a boom in the global market. Mounting evidence suggests that approaches based on several different domains should move towards the goal of early diagnosis of individuals affected by neurodevelopmental disorders. Using an interdisciplinary and collaborative approach in diagnosis and support can resolve many hurdles such as lack of awareness, transport, and financial burdens by being made available to individuals at the onset of symptoms. Interdisciplinary Approaches to Altering Neurodevelopmental Disorders is a pivotal reference source that explores neurodevelopmental disorders and a diverse array of diagnostic tools and therapies assisted by neurotechnological devices. While covering a wide range of topics including individual-centered design, artificial intelligence, and multifaceted therapies, this book is ideally designed for neuroscientists, medical practitioners, clinical psychologists, special educators, counselors, therapists, researchers, academicians, and students.
Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud service providers and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find good solutions quickly in a restricted area. However, the heuristics optimization methods do not provide a suitable solution for the scheduling problem. Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems. In order to minimize the defined objective of task resource mapping, improved versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling performance with less computational burden. In recent years, PSO has been successfully applied to solve different kinds of problems. It is famous for its easy realization and fast convergence, while suffering from the possibility of early convergence to local optimums. In the proposed Improved Particle Swarm Optimization (IPSO) algorithm, whenever early convergence occurs, the original particle swarm would be considered the worst positions an individual particle and worst positions global particle the whole swarm have experienced.
Evaluating the performance of various types of hyperspectral vegetation indices in characterizing agricultural crops, this volume discusses non-invasive quantification of foliar pigments, leaf nitrogen concentration of cereal crop, the estimation of nitrogen content in crops and pastures, forest leaf chlorophyll content, among others. Each chapter reviews existing “state-of-art” knowledge, highlights the advances made, and provides guidance for appropriate use of hyperspectral images in study of vegetation. The concluding chapter provides readers with the editor’s view and guidance on the highlights and the essence of the Volume 2 and the editor’s perspective.
Advances in Computing, Communication, Automation and Biomedical Technology aims to bring together leading academic, scientists, researchers, industry representatives, postdoctoral fellows and research scholars around the world to share their knowledge and research expertise, to advances in the areas of Computing, Communication, Electrical, Civil, Mechanical and Biomedical Systems as well as to create a prospective collaboration and networking on various areas. It also provides a premier interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered, and solutions adopted in the fields of innovation.
Swarm Intelligence in Cloud Computing is an invaluable treatise for researchers involved in delivering intelligent optimized solutions for reliable deployment, infrastructural stability, and security issues of cloud-based resources. Starting with a bird’s eye view on the prevalent state-of-the-art techniques, this book enriches the readers with the knowledge of evolving swarm intelligent optimized techniques for addressing different cloud computing issues including task scheduling, virtual machine allocation, load balancing and optimization, deadline handling, power-aware profiling, fault resilience, cost-effective design, and energy efficiency. The book offers comprehensive coverage of the most essential topics, including: Role of swarm intelligence on cloud computing services Cloud resource sharing strategies Cloud service provider selection Dynamic task and resource scheduling Data center resource management. Indrajit Pan is an Associate Professor in Information Technology of RCC Institute of Information Technology, India. He received his PhD from Indian Institute of Engineering Science and Technology, Shibpur, India. With an academic experience of 14 years, he has published around 40 research publications in different international journals, edited books, and conference proceedings. Mohamed Abd Elaziz is a Lecturer in the Mathematical Department of Zagazig University, Egypt. He received his PhD from the same university. He is the author of more than 100 articles. His research interests include machine learning, signal processing, image processing, cloud computing, and evolutionary algorithms. Siddhartha Bhattacharyya is a Professor in Computer Science and Engineering of Christ University, Bangalore. He received his PhD from Jadavpur University, India. He has published more than 230 research publications in international journals and conference proceedings in his 20 years of academic experience.
In our abundant computing infrastructure, performance improvements across most all application spaces are now severely limited by the energy dissipation involved in processing, storing, and moving data. The exponential increase in the volume of data to be handled by our computational infrastructure is driven in large part by unstructured data from countless sources. This book explores revolutionary device concepts, associated circuits, and architectures that will greatly extend the practical engineering limits of energy-efficient computation from device to circuit to system level. With chapters written by international experts in their corresponding field, the text investigates new approaches to lower energy requirements in computing. Features • Has a comprehensive coverage of various technologies • Written by international experts in their corresponding field • Covers revolutionary concepts at the device, circuit, and system levels
Written by leading global experts, including pioneers in the field, the four-volume set on Hyperspectral Remote Sensing of Vegetation, Second Edition, reviews existing state-of-the-art knowledge, highlights advances made in different areas, and provides guidance for the appropriate use of hyperspectral data in the study and management of agricultural crops and natural vegetation. Volume I, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. Volume II, Hyperspectral Indices and Image Classifications for Agriculture and Vegetation evaluates the performance of hyperspectral narrowband or imaging spectroscopy data with specific emphasis on the uses and applications of hyperspectral narrowband vegetation indices in characterizing, modeling, mapping, and monitoring agricultural crops and vegetation. Volume III, Biophysical and Biochemical Characterization and Plant Species Studies demonstrates the methods that are developed and used to study terrestrial vegetation using hyperspectral data. This volume includes extensive discussions on hyperspectral data processing and how to implement data processing mechanisms for specific biophysical and biochemical applications such as crop yield modeling, crop biophysical and biochemical property characterization, and crop moisture assessments. Volume IV, Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation discusses the use of hyperspectral or imaging spectroscopy data in numerous specific and advanced applications, such as forest management, precision farming, managing invasive species, and local to global land cover change detection.
Business and IT organizations are currently embracing new strategically sound concepts in order to be more customer-centric, competitive, and cognitive in their daily operations. While useful, the various software tools, pioneering technologies, as well as their unique contributions largely go unused due to the lack of information provided on their special characteristics. Novel Practices and Trends in Grid and Cloud Computing is a collection of innovative research on the key concerns of cloud computing and how they are being addressed, as well as the various technologies and tools empowering cloud theory to be participative, penetrative, pervasive, and persuasive. While highlighting topics including cyber security, smart technology, and artificial intelligence, this book is ideally designed for students, researchers, and business managers on the lookout for innovative IT solutions for all the business automation software and improvisations of computational technologies.
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.