Publication
Dynamic Semantic Data Replication for K-Random Search in Peer-to-Peer Networks
Xiaoqi Cao; Matthias Klusch
In: 11th IEEE International Symposium on Network Computing and Applications (NCA). International Symposium on Network Computing and Applications (NCA-12), 11th, August 23-25, Cambridge, MA, USA, Pages 20-27, ISBN 978-1-4673-2214-0, IEEE Computer Society, 2012.
Abstract
We present a dynamic semantic data replication scheme
called DSDR for classic k-random search in unstructured peer-to-peer
(P2P) networks. During its k-random search each peer
periodically updates its local view on the semantic overlay
of the network based on observed queries (demand) and
received information about provided items (supply), in particular their
semantics. Peers dynamically form potentially overlapping
groups for semantically equivalent or similar items they are
actually demanding. Besides, each peer predicts the number
of needed item replicas in the future based on its local
observations in the past.
The decision of which item to best replicate to which
member is made within each demander group
based on the maximal expected utility, traffic costs,
and plausibility of such replication.
Our experimental evaluation evidences that k-random search
with DSDR-based replication can significantly outperform
its combination with a near-optimal but
non-semantic replication strategy, as well as a peer expertise-based
semantic P2P search without replication.