PFRF: An adaptive data replication algorithm based on star-topology data grids
Future Generation Computer Systems
Adaptive service node placements in P2P-like architectures
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
The Journal of Supercomputing
Caju: a content distribution system for edge networks
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
Distributed caching in unstructured peer-to-peer file sharing networks
Computers and Electrical Engineering
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In peer-to-peer file sharing systems, file replication technology is widely used to reduce hot spots and improve file query efficiency. Most current file replication methods replicate files in all nodes or two end points on a client-server query path. However, these methods either have low effectiveness or come at a cost of high overhead. File replication in server side enhances replica hit rate, hence, lookup efficiency but produces overloaded nodes and cannot significantly reduce query path length. File replication in client side could greatly reduce query path length, but cannot guarantee high replica hit rate to fully utilize replicas. Though replication along query path solves these problems, it comes at a high cost of overhead due to more replicas and produces underutilized replicas. This paper presents an Efficient and Adaptive Decentralized (EAD) file replication algorithm that achieves high query efficiency and high replica utilization at a significantly low cost. EAD enhances the utilization of file replicas by selecting query traffic hubs and frequent requesters as replica nodes, and dynamically adapting to nonuniform and time-varying file popularity and node interest. Unlike current methods, EAD creates and deletes replicas in a decentralized self-adaptive manner while guarantees high replica utilization. Theoretical analysis shows the high performance of EAD. Simulation results demonstrate the efficiency and effectiveness of EAD in comparison with other approaches in both static and dynamic environments. It dramatically reduces the overhead of file replication, and yields significant improvements on the efficiency and effectiveness of file replication in terms of query efficiency, replica hit rate, and overloaded nodes reduction.