What's the difference? Efficient set reconciliation without prior context
ACM SIGCOMM Computer Communication Review, 2011•dl.acm.org
We describe a synopsis structure, the Difference Digest, that allows two nodes to compute
the elements belonging to the set difference in a single round with communication overhead
proportional to the size of the difference times the logarithm of the keyspace. While set
reconciliation can be done efficiently using logs, logs require overhead for every update and
scale poorly when multiple users are to be reconciled. By contrast, our abstraction assumes
no prior context and is useful in networking and distributed systems applications such as …
the elements belonging to the set difference in a single round with communication overhead
proportional to the size of the difference times the logarithm of the keyspace. While set
reconciliation can be done efficiently using logs, logs require overhead for every update and
scale poorly when multiple users are to be reconciled. By contrast, our abstraction assumes
no prior context and is useful in networking and distributed systems applications such as …
We describe a synopsis structure, the Difference Digest, that allows two nodes to compute the elements belonging to the set difference in a single round with communication overhead proportional to the size of the difference times the logarithm of the keyspace. While set reconciliation can be done efficiently using logs, logs require overhead for every update and scale poorly when multiple users are to be reconciled. By contrast, our abstraction assumes no prior context and is useful in networking and distributed systems applications such as trading blocks in a peer-to-peer network, and synchronizing link-state databases after a partition.
Our basic set-reconciliation method has a similarity with the peeling algorithm used in Tornado codes [6], which is not surprising, as there is an intimate connection between set difference and coding. Beyond set reconciliation, an essential component in our Difference Digest is a new estimator for the size of the set difference that outperforms min-wise sketches [3] for small set differences.
Our experiments show that the Difference Digest is more efficient than prior approaches such as Approximate Reconciliation Trees [5] and Characteristic Polynomial Interpolation [17]. We use Difference Digests to implement a generic KeyDiff service in Linux that runs over TCP and returns the sets of keys that differ between machines.
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