I have recently had the opportunity to present DIAS, the Dynamic Intelligent Aggregation Service at TU Dresden. I was invited to give this talk by Professor Christof Fetzer. His group has developed the XSiena Bloom Filter library used by DIAS to detect aggregation inconsistencies that cause inaccuracies to the computed aggregates. My interaction with Professor Fetzer and his group has been very constructive by having an in-depth discussion and exchanging various ideas about DIAS, its performance and fault-teolerance.
An abstract of my talk follows:
The computation of aggregate information provides crucial management information to distributed applications. Aggregate information may concern measurements such as the average load, the maximum bandwidth capacity or the size of a network/system. Performing centralized aggregation is straightforward as the input sample of an aggregation function is available at a single location. However, centralized aggregation is not always an option or desired. Aggregation of information distributed in large-scale decentralized systems of arbitrary interconnected nodes is challenging. Existing methodologies for decentralized aggregation have three crucial limitations: (i) They are designed to compute specific aggregation functions, e.g. average or count. (ii) They are based on specific routing mechanisms, e.g. trees, to prevent duplicates. (iii) They cannot adapt the aggregates in case the input sample changes, and therefore they perform expensive recomputations. This presentation illustrates an alternative mechanism for decentralized aggregation: DIAS, the Dynamic Intelligent Aggregation Service. DIAS is able to compute almost any aggregation function over an unstructured overlay network and under a dynamically changing input sample. Both (i) duplicate and (ii) outdated input values are detected by introducing a distributed consistency mechanism based on probabilistic data structures: bloom filters. Experimental evaluation shows that DIAS computes aggregates accurately even under the effect of false positives in the bloom filters.
Tags: aggregation, bloom filter, DIAS, overlay network
This entry was posted on Wednesday, March 21st, 2012 at 23:41
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