Guide to Distributed Algorithms: Design, Analysis and Implementation Using PythonКНИГИ » ПРОГРАММИНГ
Название: Guide to Distributed Algorithms: Design, Analysis and Implementation Using Python Автор: K. Erciyes Издательство: Springer Серия: Undergraduate Topics in Computer Science Год: 2025 Страниц: 298 Язык: английский Формат: pdf (true), epub Размер: 57.2 MB
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.
This book is about the design, analysis and most importantly, the implementation of distributed algorithms which we describe in full detail at coding level using the Python programming language. We assume that the reader is exposed to the design and analysis of sequential algorithms over which the distributed algorithms may be constructed.
We follow a simple template for many algorithms introduced in the text; we review the sequential algorithm for the problem briefly and then describe the design of the distributed algorithm for the same problem followed by code in Python programming language. The text can be broadly divided into Background, Fundamental Algorithms, Distributed Graph Algorithms and Applications parts. The Background part is mainly about distributed system concepts, analysis of distributed algorithms and coding templates that will be used in proceeding parts. The Fundamental Algorithms part contains the main distributed algorithm problems such as synchronization, mutual exclusion and fault tolerance. The Distributed Graph Algorithms part consists of three chapters with algorithms on trees and traversals, weighted graphs and graph decomposition. The final part contains distributed algorithms for real-life problems in two major areas of application: mobile ad hoc networks and wireless sensor networks.
Python programming language is selected for the implementation because of its simplicity and for having rich set of libraries that aid to hide low level details such as data manipulations. The mpi4py module of Python is used for distributed algorithm implementations which provides interface for the commonly used Message Passing Interface (MPI) routines. For almost all algorithms, we provide Python code that can be modified and tested for various inputs. All of the Python code can be easily transported to a real distributed environment with the possible modification of send and receive communication procedures of the actual environment, a simple way to achieve this goal is to use the basic socket communications. In a distributed application that uses Message Passing Interface for network communications, the modifications are trivial. The codes are not optimized and brevity is forsaken for clarity in many cases. In many cases, we implement similar problems using different models such as finite state machines or just plain code to achieve the same result to display different ways of obtaining the same output. The codes are tested rigorously for various sample graphs, however, as it frequently happens with any software, errors are possible and I would be happy to know any bugs.
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. The intended audience for this book is the senior/graduate students of computer science, electrical and electronic engineering, bioinformatics and any researcher or a person with background in discrete mathematics, basic graph theory and algorithms.
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