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Guide to distributed algorithms = de...

Erciyes, K.

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Guide to distributed algorithms = design, analysis and implementation using Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Guide to distributed algorithms/ by K. Erciyes.
Reminder of title:	design, analysis and implementation using Python /
Author:	Erciyes, K.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 298 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part I: Background - 1. Introduction -- 2. Basic Concepts -- 3. Models -- Part II: Fundamental Algorithms -- 4. Time Management -- 5. Distributed Mutual Exclusion -- 6. Distributed Snapshots and Global States -- 7. Coordination -- 8. Fault Tolerance -- 9. Consensus and Agreement -- 10. Multicast Communication and Message Ordering -- 11. Distributed Transactions and Replication -- Part III: Distributed Graph Algorithms - 12. Trees and Traversals -- 13. Weighted Graphs -- 14. Graph Decomposition -- Part IV: Applications -- 15. Mobile Ad hoc Networks -- 16. Wireless Sensor Networks. 17. The Internet and the Internet of Things.
Contained By:	Springer Nature eBook
Subject:	Distributed algorithms. -
Online resource:	https://doi.org/10.1007/978-3-031-79018-8
ISBN:	9783031790188

Guide to distributed algorithms = design, analysis and implementation using Python /
Erciyes, K.

Guide to distributed algorithmsdesign, analysis and implementation using Python /[electronic resource] :by K. Erciyes. - Cham :Springer Nature Switzerland :2025. - xiv, 298 p. :ill. (some col.), digital ;24 cm. - Undergraduate topics in computer science,2197-1781. - Undergraduate topics in computer science..

Part I: Background - 1. Introduction -- 2. Basic Concepts -- 3. Models -- Part II: Fundamental Algorithms -- 4. Time Management -- 5. Distributed Mutual Exclusion -- 6. Distributed Snapshots and Global States -- 7. Coordination -- 8. Fault Tolerance -- 9. Consensus and Agreement -- 10. Multicast Communication and Message Ordering -- 11. Distributed Transactions and Replication -- Part III: Distributed Graph Algorithms - 12. Trees and Traversals -- 13. Weighted Graphs -- 14. Graph Decomposition -- Part IV: Applications -- 15. Mobile Ad hoc Networks -- 16. Wireless Sensor Networks. 17. The Internet and the Internet of Things.

The study of distributed algorithms provides the needed background in many real-life applications, such as: distributed real-time systems, wireless sensor networks, mobile ad hoc networks and distributed databases. The main goal of Guide to Distributed Algorithms is to provide a detailed study of the design and analysis methods of distributed algorithms and to supply the implementations of most of the presented algorithms in Python language, which is the unique feature of the book not found in any other contemporary books on distributed computing. Topics and features: Presents comprehensive design methods for distributed algorithms Provides detailed analysis for the algorithms presented Uses graph templates to demonstrate the working of algorithms Provides working Python code for most of the algorithms presented This unique textbook/study manual can serve as a comprehensive manual of distributed algorithms for Computer Science and non-CS majors as well as practitioners of distributed algorithms in research projects. Dr. K. Erciyes is a professor of Computer Engineering at Yaşar University, İzmir, Turkiye. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics, Guide to Graph Algorithms, Distributed Real-Time Systems, Discrete Mathematics and Graph Theory and Algebraic Graph Algorithms.

ISBN: 9783031790188

Standard No.: 10.1007/978-3-031-79018-8doiSubjects--Topical Terms:

1236056
Distributed algorithms.

LC Class. No.: QA76.58

Dewey Class. No.: 005.13

Guide to distributed algorithms = design, analysis and implementation using Python /
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505 0 $a Part I: Background - 1. Introduction -- 2. Basic Concepts -- 3. Models -- Part II: Fundamental Algorithms -- 4. Time Management -- 5. Distributed Mutual Exclusion -- 6. Distributed Snapshots and Global States -- 7. Coordination -- 8. Fault Tolerance -- 9. Consensus and Agreement -- 10. Multicast Communication and Message Ordering -- 11. Distributed Transactions and Replication -- Part III: Distributed Graph Algorithms - 12. Trees and Traversals -- 13. Weighted Graphs -- 14. Graph Decomposition -- Part IV: Applications -- 15. Mobile Ad hoc Networks -- 16. Wireless Sensor Networks. 17. The Internet and the Internet of Things.
520 $a The study of distributed algorithms provides the needed background in many real-life applications, such as: distributed real-time systems, wireless sensor networks, mobile ad hoc networks and distributed databases. The main goal of Guide to Distributed Algorithms is to provide a detailed study of the design and analysis methods of distributed algorithms and to supply the implementations of most of the presented algorithms in Python language, which is the unique feature of the book not found in any other contemporary books on distributed computing. Topics and features: Presents comprehensive design methods for distributed algorithms Provides detailed analysis for the algorithms presented Uses graph templates to demonstrate the working of algorithms Provides working Python code for most of the algorithms presented This unique textbook/study manual can serve as a comprehensive manual of distributed algorithms for Computer Science and non-CS majors as well as practitioners of distributed algorithms in research projects. Dr. K. Erciyes is a professor of Computer Engineering at Yaşar University, İzmir, Turkiye. His other publications include the Springer titles Distributed Graph Algorithms for Computer Networks, Distributed and Sequential Algorithms for Bioinformatics, Guide to Graph Algorithms, Distributed Real-Time Systems, Discrete Mathematics and Graph Theory and Algebraic Graph Algorithms.
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