A distributed system consists of many heterogeneous processors with different processing power and all processors are interconnected with a communication channel. In such a system, if some processors are less loaded or idle and others are heavily loaded, the system performance will be reduced drastically. System performance can be improved by using proper load balancing [1, 4]. The aim of load balancing is to improve the performance measures and reduce the overall completion time and cost
This book constitutes the proceedings of the 24th International Conference on Theory and Applications of Satisfiability Testing, SAT 2021, which took place in Barcelona, Spain, in July 2021. The 37 full papers presented in this volume were carefully reviewed and selected from 73 submissions. They deal with theory and applications of the propositional satisfiability problem, broadly construed. Aside from plain propositional satisfiability, the scope of the meeting includes Boolean optimization, including MaxSAT and pseudo-Boolean (PB) constraints, quantified Boolean formulas (QBF), satisfiability modulo theories (SMT), and constraint programming (CP) for problems with clear connections to Boolean reasoning.
Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond.
This text constitutes the proceedings from the International Workshop on Autonomous Decentralized Systems (IWADS2000) that took place in 2000. Topics covered include flexible and autonomous service replication technique, and information searching in autonomous mobile agent groups.
This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2013, held Boston, MA, USA, in May 2013. The 10 revised papers presented were carefully reviewed and selected from 20 submissions. The papers cover the following topics parallel scheduling for commercial environments, scientific computing, supercomputing and cluster platforms.
Distributed and Cloud Computing: From Parallel Processing to the Internet of Things offers complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing. It is the first modern, up-to-date distributed systems textbook; it explains how to create high-performance, scalable, reliable systems, exposing the design principles, architecture, and innovative applications of parallel, distributed, and cloud computing systems. Topics covered by this book include: facilitating management, debugging, migration, and disaster recovery through virtualization; clustered systems for research or ecommerce applications; designing systems as web services; and social networking systems using peer-to-peer computing. The principles of cloud computing are discussed using examples from open-source and commercial applications, along with case studies from the leading distributed computing vendors such as Amazon, Microsoft, and Google. Each chapter includes exercises and further reading, with lecture slides and more available online. This book will be ideal for students taking a distributed systems or distributed computing class, as well as for professional system designers and engineers looking for a reference to the latest distributed technologies including cloud, P2P and grid computing. - Complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing - Includes case studies from the leading distributed computing vendors: Amazon, Microsoft, Google, and more - Explains how to use virtualization to facilitate management, debugging, migration, and disaster recovery - Designed for undergraduate or graduate students taking a distributed systems course—each chapter includes exercises and further reading, with lecture slides and more available online
The area of communication and computer networks has become a very active field of research by the control systems community in the last years. Tools from convex optimization and control theory are playing increasing roles in efficient network utilization, fair resource allocation, and communication delay accommodation and the field of Networked Control systems is fast becoming a mainstay of control systems research and applications. This carefully edited book brings together solicited contributions from experts in the various areas of communication/control networks referring to both networks under control (control in networks) as well as networked control systems (control over networks). The aim of this book is to reverse the trend of fragmentation and specialization in Communication Control Networks connecting various interdisciplinary research fields including control, communication, applied mathematics and computer science.
This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
