Distributed Algorithms
Epidemic-Style Proactive Aggregation in Large Overlay Networks
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Gossip-based aggregation in large dynamic networks
ACM Transactions on Computer Systems (TOCS)
Bernoulli sampling based (ε, δ)-approximate aggregation in large-scale sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Adam2: Reliable Distribution Estimation in Decentralised Environments
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
Gossip-based distribution estimation in peer-to-peer networks
IPTPS'08 Proceedings of the 7th international conference on Peer-to-peer systems
Fault-Tolerant Aggregation for Dynamic Networks
SRDS '10 Proceedings of the 2010 29th IEEE Symposium on Reliable Distributed Systems
Fault-Tolerant aggregation: flow-updating meets mass-distribution
OPODIS'11 Proceedings of the 15th international conference on Principles of Distributed Systems
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The distributed aggregation of simple aggregates such as minima/maxima, counts, sums and averages have been studied in the past and are important tools for distributed algorithms and network coordination. Nonetheless, this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates. This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties: robustness when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property and with churn, without requiring restarts. The proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique.