Distributed Computing and Optimization Techniques

Distributed Computing and Optimization Techniques PDF Author: Sudhan Majhi
Publisher: Springer Nature
ISBN: 9811922810
Category : Computers
Languages : en
Pages : 855

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Book Description
This book introduces research presented at the International Conference on Distributed Computing and Optimization Techniques (ICDCOT–2021), a two-day conference, where researchers, engineers, and academicians from all over the world came together to share their experiences and findings on all aspects of distributed computing and its applications in diverse areas. The book includes papers on distributed computing, intelligent system, optimization method, mathematical modeling, fuzzy logic, neural networks, grid computing, load balancing, communication. It will be a valuable resource for students, academics, and practitioners in the industry working on distributed computing.

Distributed Computing and Optimization Techniques

Distributed Computing and Optimization Techniques PDF Author: Sudhan Majhi
Publisher: Springer Nature
ISBN: 9811922810
Category : Computers
Languages : en
Pages : 855

Get Book Here

Book Description
This book introduces research presented at the International Conference on Distributed Computing and Optimization Techniques (ICDCOT–2021), a two-day conference, where researchers, engineers, and academicians from all over the world came together to share their experiences and findings on all aspects of distributed computing and its applications in diverse areas. The book includes papers on distributed computing, intelligent system, optimization method, mathematical modeling, fuzzy logic, neural networks, grid computing, load balancing, communication. It will be a valuable resource for students, academics, and practitioners in the industry working on distributed computing.

Optimization Techniques for Solving Complex Problems

Optimization Techniques for Solving Complex Problems PDF Author: Enrique Alba
Publisher: John Wiley & Sons
ISBN: 0470293322
Category : Computers
Languages : en
Pages : 500

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Book Description
Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One—covers methodologies for complex problem solving including genetic programming, neural networks, genetic algorithms, hybrid evolutionary algorithms, and more. Part Two—delves into applications including DNA sequencing and reconstruction, location of antennae in telecommunication networks, metaheuristics, FPGAs, problems arising in telecommunication networks, image processing, time series prediction, and more. All chapters contain examples that illustrate the applications themselves as well as the actual performance of the algorithms.?Optimization Techniques for Solving Complex Problems is a valuable resource for practitioners and researchers who work with optimization in real-world settings.

Parallel Combinatorial Optimization

Parallel Combinatorial Optimization PDF Author: El-Ghazali Talbi
Publisher: John Wiley & Sons
ISBN: 0470053917
Category : Computers
Languages : en
Pages : 348

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Book Description
This text provides an excellent balance of theory and application that enables you to deploy powerful algorithms, frameworks, and methodologies to solve complex optimization problems in a diverse range of industries. Each chapter is written by leading experts in the fields of parallel and distributed optimization. Collectively, the contributions serve as a complete reference to the field of combinatorial optimization, including details and findings of recent and ongoing investigations.

Parallel and Distributed Computation: Numerical Methods

Parallel and Distributed Computation: Numerical Methods PDF Author: Dimitri Bertsekas
Publisher: Athena Scientific
ISBN: 1886529159
Category : Mathematics
Languages : en
Pages : 832

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Book Description
This highly acclaimed work, first published by Prentice Hall in 1989, is a comprehensive and theoretically sound treatment of parallel and distributed numerical methods. It focuses on algorithms that are naturally suited for massive parallelization, and it explores the fundamental convergence, rate of convergence, communication, and synchronization issues associated with such algorithms. This is an extensive book, which aside from its focus on parallel and distributed algorithms, contains a wealth of material on a broad variety of computation and optimization topics. It is an excellent supplement to several of our other books, including Convex Optimization Algorithms (Athena Scientific, 2015), Nonlinear Programming (Athena Scientific, 1999), Dynamic Programming and Optimal Control (Athena Scientific, 2012), Neuro-Dynamic Programming (Athena Scientific, 1996), and Network Optimization (Athena Scientific, 1998). The on-line edition of the book contains a 95-page solutions manual.

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers PDF Author: Stephen Boyd
Publisher: Now Publishers Inc
ISBN: 160198460X
Category : Computers
Languages : en
Pages : 138

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Book Description
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.

Concurrent and Distributed Computing in Java

Concurrent and Distributed Computing in Java PDF Author: Vijay K. Garg
Publisher: John Wiley & Sons
ISBN: 0471721263
Category : Computers
Languages : en
Pages : 331

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Book Description
Concurrent and Distributed Computing in Java addresses fundamental concepts in concurrent computing with Java examples. The book consists of two parts. The first part deals with techniques for programming in shared-memory based systems. The book covers concepts in Java such as threads, synchronized methods, waits, and notify to expose students to basic concepts for multi-threaded programming. It also includes algorithms for mutual exclusion, consensus, atomic objects, and wait-free data structures. The second part of the book deals with programming in a message-passing system. This part covers resource allocation problems, logical clocks, global property detection, leader election, message ordering, agreement algorithms, checkpointing, and message logging. Primarily a textbook for upper-level undergraduates and graduate students, this thorough treatment will also be of interest to professional programmers.

Parallel and Distributed Computing: Applications and Technologies

Parallel and Distributed Computing: Applications and Technologies PDF Author: K. M. Liew
Publisher: Springer Science & Business Media
ISBN: 3540240136
Category : Computers
Languages : en
Pages : 914

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Book Description
This book constitutes the refereed proceedings of the 5th International Conference on Parallel and Distributed Computing, Applications and Technologies; PDCAT 2004, held in Singapore in December 2004. The 173 papers presented were carefully reviewed and selected from 242 submissions. The papers focus on parallel and distributed computing from the perspectives of algorithms, networking and architecture, software systems and technologies, and applications. Besides classical topics from high performance computing, major recent developments are addressed, such as molecular computing, date mining, knowledge discovery, optical networks, secure computing and communications, wireless networks, mobile computing, component-based systems, Internet computing, and Web Technologies.

Machine Learning and Optimization Models for Optimization in Cloud

Machine Learning and Optimization Models for Optimization in Cloud PDF Author: Punit Gupta
Publisher: CRC Press
ISBN: 1000542254
Category : Computers
Languages : en
Pages : 219

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Book Description
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.

Parallel and Distributed Computing, Applications and Technologies

Parallel and Distributed Computing, Applications and Technologies PDF Author: Jong Hyuk Park
Publisher: Springer
ISBN: 9811359075
Category : Computers
Languages : en
Pages : 493

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Book Description
This book constitutes the refereed proceedings of the 19th International Conference on CParallel and Distributed Computing, Applications and Technologies, PDCAT 2018, held in Jeju Island, South Korea, in August 2018. The 35 revised full papers presented along with the 14 short papers and were carefully reviewed and selected from 150 submissions. The papers of this volume are organized in topical sections on wired and wireless communication systems, high dimensional data representation and processing, networks and information security, computing techniques for efficient networks design, electronic circuits for communication systems.

Models for Parallel and Distributed Computation

Models for Parallel and Distributed Computation PDF Author: R. Correa
Publisher: Springer Science & Business Media
ISBN: 1475736096
Category : Computers
Languages : en
Pages : 334

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Book Description
Parallel and distributed computation has been gaining a great lot of attention in the last decades. During this period, the advances attained in computing and communication technologies, and the reduction in the costs of those technolo gies, played a central role in the rapid growth of the interest in the use of parallel and distributed computation in a number of areas of engineering and sciences. Many actual applications have been successfully implemented in various plat forms varying from pure shared-memory to totally distributed models, passing through hybrid approaches such as distributed-shared memory architectures. Parallel and distributed computation differs from dassical sequential compu tation in some of the following major aspects: the number of processing units, independent local dock for each unit, the number of memory units, and the programming model. For representing this diversity, and depending on what level we are looking at the problem, researchers have proposed some models to abstract the main characteristics or parameters (physical components or logical mechanisms) of parallel computers. The problem of establishing a suitable model is to find a reasonable trade-off among simplicity, power of expression and universality. Then, be able to study and analyze more precisely the behavior of parallel applications.