Randomized algorithms
Performing Work Efficiently in the Presence of Faults
SIAM Journal on Computing
Fault-Tolerant Parallel Computation
Fault-Tolerant Parallel Computation
Randomization helps to perform independent tasks reliably
Random Structures & Algorithms
Optimal decision strategies in Byzantine environments
Journal of Parallel and Distributed Computing
Reliably Executing Tasks in the Presence of Untrusted Entities
SRDS '06 Proceedings of the 25th IEEE Symposium on Reliable Distributed Systems
Do-All Computing in Distributed Systems
Do-All Computing in Distributed Systems
Toward Maximizing the Quality of Results of Dependent Tasks Computed Unreliably
Theory of Computing Systems
On the bit communication complexity of randomized rumor spreading
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Robust network supercomputing with malicious processes
DISC'06 Proceedings of the 20th international conference on Distributed Computing
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
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Internet supercomputing provides means for harnessing the power of a vast number of interconnected computers. With this come the challenges of marshaling distributed resources and dealing with failures. Traditional centralized approaches employ a master processor and many worker processors that execute a collection of tasks on behalf of the master. Despite the simplicity and advantages of centralized schemes, the master processor is a performance bottleneck and a single point of failure. Additionally, a phenomenon of increasing concern is that workers may return incorrect results, e.g., due to unintended failures, over-clocked processors, or due to workers claiming to have performed work to obtain a high rank in the system. This paper develops an original approach that eliminates the master and instead uses a decentralized algorithm, where workers cooperate in performing tasks. The failure model assumes that the average probability of a worker returning a wrong result is inferior to 1/2. We present a randomized synchronous algorithm for n processors and t tasks (t≥n) achieving time complexity $\Theta(\frac{t}{n} \log n)$ and work Θ(tlogn). It is shown that upon termination the workers know the results of all tasks with high probability, and that these results are correct with high probability. The message complexity of the algorithm is Θ(n logn), and the bit complexity is O(tn log3n). Simulations illustrate the behavior of the algorithm under realistic assumptions.