A Peer-to-Peer Replica Location Service Based on a Distributed Hash Table
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A Peer-to-Peer Replica Location Service with properties of self-organization, fault-tolerance and improved scalability, and a predecessor replication scheme is introduced that reduces query hotspots of popular mappings by distributing queries among nodes.
Abstract
A Replica Location Service (RLS) allows registration and discovery of data replicas. In earlier work, we proposed an RLS framework and described the performance and scalability of an RLS implementation in Globus Toolkit Version 3.0. In this paper, we present a Peer-to-Peer Replica Location Service (P-RLS) with properties of self-organization, fault-tolerance and improved scalability. P-RLS uses the Chord algorithm to self-organize PRLS servers and exploits the Chord overlay network to replicate P-RLS mappings adaptively. Our performance measurements demonstrate that update and query latencies increase at a logarithmic rate with the size of the P-RLS network, while the overhead of maintaining the P-RLS network is reasonable. Our simulation results for adaptive replication demonstrate that as the number of replicas per mapping increases, the mappings are more evenly distributed among P-RLS nodes. We introduce a predecessor replication scheme and show it reduces query hotspots of popular mappings by distributing queries among nodes.
