Spectra: robust estimation of distribution functions in networks
DAIS'12 Proceedings of the 12th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Self-adaptive approximate queries for large-scale information aggregation
International Journal of Web and Grid Services
The XtreemOS Resource Selection Service
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section: Extended Version of SASO 2011 Best Paper
ASIA: application-specific integrated aggregation for publish/subscribe middleware
Proceedings of the Posters and Demo Track
Aggregation for implicit invocations
Proceedings of the 12th annual international conference on Aspect-oriented software development
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To enable decentralised actions in very large distributed systems, it is often important to provide the nodes with global knowledge about the values of attributes across all nodes. This paper shows how, given an attribute whose values are distributed across a large decentralised system, each node can efficiently estimate the statistical distribution of these values. Simulations using heavily skewed real-world node attribute distributions show that our estimation methods outperform the state-of-the-art heuristics by an order of magnitude with an average error of 0.05% and a maximum error of 2%. To obtain this accuracy, each node sends on average just 120 kB of data independent of the system size. Our algorithms also achieve this accuracy in the presence of heavy churn of system membership. Furthermore, our algorithm enables self-tuning by continuously estimating the accuracy of its own distribution approximation.