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ANALYSIS STUDY ON CACHING AND REPLICA PLACEMENT ALGORITHM FOR CONTENT DISTRIBUTION IN DISTRIBUTED COMPUTING NETWORKS

Authors: Dr. Anna Saro Vijendran; S.Thavamani;

ANALYSIS STUDY ON CACHING AND REPLICA PLACEMENT ALGORITHM FOR CONTENT DISTRIBUTION IN DISTRIBUTED COMPUTING NETWORKS

Abstract

Recently there has been significant research focus on distributed computing network massively caching and replica placement problems for content distribution in globally. Peer-to-Peer (P2P) network provides dynamically decentralized, self organized, scalable objects in distributed computing system. However such networks suffer from high latency, network traffic and cache update problems. The existing caching and replica placement techniques for placing objects across peer-to-peer network have no complete solution to these problems. This paper presents an overview of the current challenges present in P2P overlay networks, followed by describes briefly the analysis study of the existing algorithms and their merits and demerits. And also suggest a new popularity based QoS-aware(Quality of Service) smart replica placement algorithm for content distribution in peer- to-peer overlay networks which overcomes the access latency, fault tolerance, network traffic and redundancy problems with low cost. The new algorithm suggested is based on the outcome of the analysis study.

Keywords

Distributed computing network, Peer-to-Peer network, Replica placement, Decentralized, Access Latency, and Network traffic, Redundancy, Fault tolerance.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green