Distributed and Parallel Computing

• Autore: Avasthi Sandhya
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2026; ©2026
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (369 pages)
• Altri autori: TripathiSuman Lata
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Distributed Systems -- 1.1 Introduction -- 1.1.1 Background and Context -- 1.1.2 Objectives of the Study -- 1.1.3 Scope and Limitations -- 1.1.4 Key Characteristics of Distributed Systems -- 1.1.4.1 Decentralization and Concurrency -- 1.1.4.2 Communication and Scalability -- 1.1.4.3 Fault Tolerance and Consistency -- 1.1.4.4 Security and Resource Sharing -- 1.1.5 Types of Distributed Systems -- 1.1.5.1 Systems for Clients and Servers -- 1.1.5.2 Peer-To-Peer Systems -- 1.1.5.3 Middleware-Based Systems -- 1.1.5.4 Three-Tier and N-Tier -- 1.1.5.5 Grid and Cloud Computing -- 1.1.6 Distributed Algorithms for Boolean Equations Over Networks -- 1.1.6.1 System of Boolean Equations Over a Network -- 1.1.6.2 Locally Private Distributed Algorithm -- 1.1.6.3 Consensus Projection for Linear Algebraic Equations -- 1.1.6.4 Affine Subspace Boolean Vector Search -- 1.1.6.5 Distributed SAT Verification -- 1.1.6.6 Distributed Equilibrium Computation of Boolean Networks -- 1.1.7 Effective Communication in Dispersed ML -- 1.1.7.1 Data Parallelism with Gradient Compression -- 1.1.7.2 Scheduling Issues -- 1.1.7.3 Decentralized Analytics -- 1.1.7.4 Federated Learning -- 1.1.7.5 Distributed Security and Privacy in Mobile Networks and IoT Systems -- 1.1.8 DS Applications Across a Wide Range of Domains -- 1.1.8.1 Web Services and Mobile Apps -- 1.1.8.2 Databases and Distributed File Storage Systems -- 1.1.8.3 Blockchain Platforms -- 1.1.8.4 Financial Services -- 1.1.8.5 Scientific Research and Simulation -- 1.1.9 Challenges and Considerations -- 1.1.9.1 Scalability of Distributed Systems in Various Applications -- 1.1.9.2 Integration with Existing Systems -- 1.1.9.3 Regulatory Compliance and Legal Framework -- 1.1.10 Future Prospects and Trends. ; 1.1.10.1 Distributed Systems Emerging Technologies (Quantum Distributed Computing) -- 1.1.10.2 Research Advancements -- 1.1.10.3 Anticipated Benefits and Challenges -- 1.1.11 Conclusion -- 1.1.11.1 Recap of Key Findings -- 1.1.11.2 Implications for Healthcare Transformation -- References -- Chapter 2 Topology in Network Technologies -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Network Topology Design -- 2.4 Advantages of Network Topologies -- 2.5 Case Studies of Network Topologies -- 2.6 Distributed and Parallel Computing in Network Topology -- 2.6.1 Challenges/Issues in Network Topology in Distributed Computing or Parallel Computing -- 2.7 Conclusion -- References -- Chapter 3 Distributed Processing Technology and Advancements -- 3.1 Introduction -- 3.2 Distributed Processing in Modern Computing -- 3.3 Evolution of Distributed Processing -- 3.4 Key Concepts and Technologies Driving the Evolution of Distributed Processing -- 3.5 Recent Advancements in Distributed Processing -- 3.6 Changing Landscape of Computing -- 3.6.1 New Opportunities and Challenges for Developers and Businesses -- 3.6.2 Use of Distributed and Parallel Computing -- 3.7 Security and Privacy Considerations -- 3.7.1 Case Studies -- 3.7.2 Future Directions -- 3.8 Opportunities and Challenges in Distributed Computing -- 3.8.1 Future Directions and Opportunity -- 3.8.2 Challenges -- 3.9 Conclusion -- References -- Chapter 4 Distributed System Architecture and Computing Models -- 4.1 Introduction -- 4.2 Distributed System Architecture -- 4.2.1 Architecture Style -- 4.2.2 Middleware Organization -- 4.3 Middleware in Distributed Systems -- 4.3.1 Host Infrastructure Middleware -- 4.3.2 Distribution Middleware -- 4.3.3 Domain-Specific Middleware -- 4.3.4 Intelligent Middleware -- 4.4 Distributed Cloud Architecture -- 4.5 Distributed Machine Learning -- 4.6 Conclusion -- References. ; Chapter 5 Parallel Computing Models and Architecture -- 5.1 Introduction -- 5.2 Evolution of Parallel Computing Models -- 5.2.1 Basic Approach Parallel Computing -- 5.2.2 Architecture -- 5.2.3 Flowchart Analysis -- 5.3 Parallel Computing Models -- 5.3.1 Complexity -- 5.3.2 Advantages -- 5.3.3 Disadvantages -- 5.3.4 Algorithmic Problem -- 5.3.4.1 Shared Memory Cache -- 5.3.4.2 Distributed Memory Model -- 5.3.4.3 Data Parallel Model -- 5.3.4.4 Task Parallel Model -- 5.3.5 Pipeline Model -- 5.3.6 Hybrid Models -- References -- Chapter 6 Network Issues and High-Level Communication Tools in Distributed Computing -- 6.1 Introduction -- 6.1.1 Common Network Issues in Distributed Computing -- 6.1.2 Group Communication in Distributed Systems -- 6.2 Latency in Distributed and Parallel Computing -- 6.3 Straggler Effect -- 6.4 Packet Loss in the Distributed Network -- 6.4.1 Implications of Packet Loss -- 6.4.2 Identifying Packet Loss -- 6.4.3 Tools to Measure Packet Loss -- 6.4.4 Techniques to Prevent Packet Loss -- 6.5 Network Congestion -- 6.5.1 Causes of Network Congestion and Implications -- 6.5.2 Network Congestion Implication -- 6.6 Communication Load -- 6.6.1 A Deterministic Strategy -- 6.6.2 Bidding Strategy -- 6.6.3 Drafting Strategy -- 6.6.4 Greedy Strategy -- 6.6.5 Threshold Strategy -- 6.7 Conclusion -- References -- Chapter 7 Infinite Horizons: Empowering Business Education Through Metaverse -- 7.1 Introduction -- 7.2 Challenges and Concerns -- 7.3 Rules of Law-Based Governance -- 7.4 Possible Implementations in Educational Framework of Metaverse Technology -- 7.5 Understanding Metaverse -- 7.6 Explore the Transformational Potential of Metaverse for Business Education -- 7.7 Innovation and Design Ideas Forum -- 7.8 Continuing Education and Career Development -- 7.9 Takeaways -- 7.10 Ethical and Legal Issues -- 7.11 Future Prospects and Innovations. ; 7.12 Conclusion -- References -- Chapter 8 Paradigm Shifts and Future Directions in Distributed Data Management for Decentralized Networks -- 8.1 Introduction -- 8.2 Paradigm Shifts in Distributed Data Management -- 8.2.1 Evolution of Data Management Paradigms -- 8.2.2 Impact of Decentralized Architectures -- 8.2.3 Key Technological Drivers -- 8.3 Emergent Architectures and Frameworks -- 8.3.1 Overview of Emergent Architectures -- 8.3.2 Domain-Specific Languages in Data Analytics -- 8.4 Integration of IoT -- 8.4.1 IoT Ecosystem and Data Management Challenges -- 8.5 Edge Computing and Big Data Analytics -- 8.5.1 Benefits of Edge Computing -- 8.5.2 Big Data Management in Distributed Systems -- 8.6 Data Aggregation and Summarization Techniques -- 8.6.1 Importance of Data Aggregation -- 8.6.2 Case Studies and Practical Implementations -- 8.7 Advanced Applications and Case Studies -- 8.7.1 Domestic and Industrial Applications -- 8.8 Future Directions in Distributed Data Management -- 8.8.1 Blockchain for Enhanced Data Security -- 8.8.2 AI and ML in Data Analytics -- 8.8.3 Prospective Research Avenues -- 8.9 Conclusion -- References -- Chapter 9 Autonomy and Adaptive Architectures in Distributed Systems -- 9.1 Introduction -- 9.2 Conceptual Framework -- 9.2.1 Definition of Key Concepts -- 9.2.1.1 Autonomy -- 9.2.1.2 Adaptive Architectures -- 9.2.1.3 Multiagent Systems -- 9.2.1.4 The Evolutionary Trajectory of MAS -- 9.2.2 Importance of Decentralized Paradigms -- 9.3 Architectural Models -- 9.3.1 Overview of Emergent Architectures -- 9.3.2 Agent-Based Models -- 9.3.2.1 Agent Architecture and Control Flow -- 9.4 Adaptive Strategies in Distributed Systems -- 9.4.1 Mechanisms for System Resilience and Scalability -- 9.5 Case Studies -- 9.5.1 Smart Grids -- 9.5.2 Geographical and Functional Infrastructure Interdependence -- 9.5.3 Water Distribution Systems. ; 9.5.3.1 Adaptive Water Management -- 9.5.3.2 Leak Detection and Management -- 9.5.3.3 Water Quality Monitoring -- 9.5.4 Transportation Systems -- 9.5.4.1 Intelligent Transportation Systems -- 9.5.4.2 Traffic Management -- 9.5.4.3 Public Transportation -- 9.6 Application of MASs -- 9.6.1 Autonomous Surface Ships -- 9.6.2 Unmanned Surface Vehicles -- 9.6.3 Swarm Robotics -- 9.6.4 Autonomous Underwater Vehicles -- 9.6.5 Hybrid MASs -- 9.6.6 Common Coordination Models in MAS -- 9.6.6.1 Centralized Coordination -- 9.6.6.2 Decentralized Coordination -- 9.6.6.3 Hierarchical Coordination -- 9.6.6.4 Market-Based Coordination -- 9.6.6.5 Contract Net Protocol -- 9.7 Autonomous Navigation Systems -- 9.7.1 Examples of Recently Developed USVs -- 9.8 Collision Avoidance Algorithms -- 9.8.1 Comparison of Collision Avoidance Algorithms -- 9.8.2 Implementation of TBA -- 9.9 Network Properties in Distributed Systems -- 9.9.1 Small-World Networks -- 9.9.2 Scale-Free Networks -- 9.9.3 Robustness and Fault Tolerance -- 9.9.4 Scalability -- 9.9.5 Application-Specific Design -- 9.10 Challenges and Future Directions -- 9.10.1 Security and Privacy Considerations -- 9.10.2 Integration with Emerging Technologies -- 9.10.2.1 Blockchain -- 9.10.2.2 Artificial Intelligence -- 9.10.2.3 Internet of Things -- 9.11 Conclusion -- References -- Chapter 10 Distributed Consensus Frequency Control in Networked Microgrid -- 10.1 Introduction -- 10.2 System Model -- 10.3 Distributed Control Technique -- 10.3.1 ANN Tuned FOPID Distributed Controller -- 10.4 Results and Discussion -- 10.5 Conclusion -- References -- Appendix -- Chapter 11 Navigating Trust in Distributed Systems -- 11.1 Introduction -- 11.1.1 Trust in Distributed Computing Ecosystem -- 11.1.2 Trust in the Internet of Things -- 11.2 Transparency Basics -- 11.3 Heterogeneous and Homogeneous DSs -- 11.3.1 Trust Concepts. ; 11.3.2 Inadequacies with Security Mechanisms.
• Sommario/riassunto: Master the growing field of distributed and parallel computing with this essential guide, offering expert insights into the fundamentals and real-world applications for intelligent and collaborative systems.
• Titolo autorizzato: Distributed and Parallel Computing
• ISBN: 1-394-28803-4; 1-394-28802-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911040927303321
• Lo trovi qui: Univ. Federico II