In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.



Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.



The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.



The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.


Table of Contents

1 Introduction 
2 Modelling I; Synchronous Network Model 
3 Leader Election in a Synchronous Ring 
4 Algorithms in General Synchronous Networks 
5 Distributed Consensus with Link Failures 
6 Distributed Consensus with Process Failures 
7 More Consensus Problems 
8 Modelling II: Asynchronous System Model 
9 Modelling III: Asynchronous Shared Memory Model 
10 Mutual Exclusion 
11 Resource Allocation 
12 Consensus 
13 Atomic Objects 
14 Modelling IV: Asynchronous Network Model 
15 Basic Asynchronous Network Algorithms 
16 Synchronizers 
17 Shared Memory versus Networks 
18 Logical Time 
19 Global Snapshots and Stable Properties 
20 Network Resource Allocation 
21 Asynchronous Networks with Process Failures 
22 Data Link Protocols 
23 Partially Synchronous System Models 
24 Mutual Exclusion with Partial Synchrony 
25 Consensus with Partial Synchrony

Distributed algorithms

Originating from basic research conducted in the 1970's and 1980's, the parallel and distributed simulation field has matured over the last few decades. Today, operational systems have been fielded for applications such as military training, analysis of communication networks, and air traffic control systems, to mention a few. The article gives an overview of technologies to distribute the execution of simulation programs over multiple computer systems. Particular emphasis is placed on synchronization (also called time management) algorithms as well as data distribution techniques.

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Parallel and Distributed Simulation Systems

A senior undergraduate or graduate level computer science textbook on algorithm design for distributed computer systems.

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An Introduction to Distributed Algorithms

This paper investigates a new shared-memory model called immediate atomic snapshot memor~. It is an extension of atomic snapshot memory in which a write operation in addition to ~vriting, also returns an atomic snapshot of the memory. Unlike regular atomic snapshot, immediate snapshot is guaranteed to closely follow the write operation. This model was previously used to obtain an inlpossibility result. Here we investigate its utility for the design of algorithms. We first implement the one-shot version in the read-write model. We then use the model to design a new renaming algorithm. The renaming algorithm we obtain is simple and requires at worst n cycles of one-shot immediate snapshots. Since our implementation of the oneshot immediate snapshot requires 0( nz) primitive read-write operations, we obtain an 0(n3) renanling algorithm. Currently the best renaming algorithm is exponential, Our implementation of the immediate snapshot relies on a priori knowledge of the bound on the *Work supported by NSF Presidential Young Investigator Award under grant DC R84-5 1396. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and Its date appear, and notice is given thet copying ie by permissi~n of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. 12th ACM Sympos!um on Principles on Distributed Computing, Ithaca NY @ 1993 ACM o-89791-613-l/93/0008/0041 . . ..$ 1.50 number of snapshots to be taken. We were not able to dispense with this condition and thus were unable to obtain an implementation of the longlived object.

Immediate atomic snapshots and fast renaming

On the Complexity of Distributed Network Decomposition

Algorithms for mutual exclusion that adapt to the current degree of contention are developed. A filter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time.

Adaptive solutions to the mutual exclusion problem

Solutions to resource allocation problems in distributed systems are examined with respect to the measures of response time, message complexity, and failure locality. Response time measures the time it takes for an algorithm to respond to the requests of a process, message complexity measures the number of messages sent and received by a process, and failure locality characterizes the size of the network that is affected by the failure of a single process. An algorithm that achieves a constant failure locality of four along with a quadratic response time and a quadratic message complexity is presented.

Efficient fault tolerant algorithms for resource allocation in distributed systems

Solutions to resource allocation problems and other related synchronization problems in distributed systems are examined with respect to the measures of response time, message complexity, and failure locality. Response time measures the time it takes for an algorithm to respond to the requests of a process; message complexity measures the number of messages sent and received by a process; and failure locality characterizes the size of the network that is affected by the failure of a single process. An algorithm for the resource allocation problem that achieves a constant failure locality of four along with a quadratic response time and a quadratic message complexity is presented. Applications of the algorithm to other process synchronization problems in distributed systems are also demonstrated.

Efficient fault-tolerant algorithms for distributed resource allocation

Algorithms for mutual exclusion that adapt to the current degree of contention are developed. Afilter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time.

A general purpose parallel programming model called mixed consistency is developed for distributed shared memory systems. This model combines two kinds of weak memory consistency conditions: causal memory and pipelined random access memory, and provides four kinds of explicit synchronization operations: read locks, write locks, barriers, and await operations. The resulting suite of memory and synchronization operations can be tailored to solve most programming problems in an efficient manner. Conditions are also developed under which the net effect of programming in this model is the same as programming with sequentially consistent memory. Several examples are included to illustrate the model and the correctness conditions.

Manhoi Choy

Papers

Adaptive solutions to the mutual exclusion problem

Efficient fault tolerant algorithms for resource allocation in distributed systems

Efficient fault-tolerant algorithms for distributed resource allocation

Adaptive solutions to the mutual exclusion problem

Mixed consistency: a model for parallel programming (extended abstract)