On processor coordination using asynchronous hardware
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
On achieving consensus using a shared memory
PODC '88 Proceedings of the seventh annual ACM Symposium on Principles of distributed computing
Bounded polynomial randomized consensus
Proceedings of the eighth annual ACM Symposium on Principles of distributed computing
Fast randomized consensus using shared memory
Journal of Algorithms
Time-and space-efficient randomized consensus
PODC '90 Proceedings of the ninth annual ACM symposium on Principles of distributed computing
Implementing fault-tolerant services using the state machine approach: a tutorial
ACM Computing Surveys (CSUR)
ACM Transactions on Programming Languages and Systems (TOPLAS)
Optimal time randomized consensus—making resilient algorithms fast in practice
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Impossibility of distributed consensus with one faulty process
Journal of the ACM (JACM)
Randomized Consensus in Expected O(n log^ 2 n) Operations Per Processor
SIAM Journal on Computing
Polylog randomized wait-free consensus
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
Efficient asynchronous consensus with the weak adversary scheduler
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
The unified structure of consensus: a layered analysis approach
PODC '98 Proceedings of the seventeenth annual ACM symposium on Principles of distributed computing
A tight lower bound for randomized synchronous consensus
PODC '98 Proceedings of the seventeenth annual ACM symposium on Principles of distributed computing
Lower bounds for distributed coin-flipping and randomized consensus
Journal of the ACM (JACM)
ACM Transactions on Computer Systems (TOCS)
Cooperative sharing and asynchronous consensus using single-reader single-writer registers
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Distributed Algorithms
Randomized Consensus in Expected O(n²log n) Operations
WDAG '91 Proceedings of the 5th International Workshop on Distributed Algorithms
Randomized protocols for asynchronous consensus
Distributed Computing - Papers in celebration of the 20th anniversary of PODC
Distributed Computing: Fundamentals, Simulations and Advanced Topics
Distributed Computing: Fundamentals, Simulations and Advanced Topics
Lower bounds for randomized consensus under a weak adversary
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Randomized consensus in expected O(n log n) individual work
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Approximate shared-memory counting despite a strong adversary
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Classifying rendezvous tasks of arbitrary dimension
Theoretical Computer Science
Approximate shared-memory counting despite a strong adversary
ACM Transactions on Algorithms (TALG)
Fast asynchronous Byzantine agreement and leader election with full information
ACM Transactions on Algorithms (TALG)
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A distributed consensus algorithm allows n processes to reach acommon decision value starting from individual inputs. Wait-free consensus, in which a process always terminates within a finite number of its own steps, is impossible in anasynchronous shared-memory system. However, consensus becomes solvable using randomization when a process only has to terminatewith probability 1. Randomized consensus algorithms are typically evaluated by their total step complexity, which is the expected total number of steps taken by all processes. This work proves that the total step complexity of randomized consensus is Θ(n2) in an asynchronous shared memory systemusing multi-writer multi-reader registers. The bound is achieved by improving both the lower and the upper bounds for this problem. In addition to improving upon the best previously known result bya factor of log2 n, the lower bound features agreatly streamlined proof. Both goals are achieved through restricting attention to a set of layered executions andusing an isoperimetric inequality for analyzing their behavior. The matching algorithm decreases the expected total step complexity by a log n factor, by leveraging themulti-writing capability of the shared registers. Its correctness proof is facilitated by viewing each execution of the algorithmas a stochastic process and applying Kolmogorov's inequality.