In P2P systems, large volumes of data are declustered
naturally across a large number of peers. But it is
very difficult to control the initial data distribution because
every user has the freedom to share any data with other
users. The system scalability can be improved by
distributing the load across multiple servers which is
proposed by replication. The large scale content distribution
systems were improved broadly using the replication
techniques. The demanded contents can be brought closer to
the clients by multiplying the source of information
geographically, which in turn reduce both the access latency
and the network traffic. Within this paper, we analyze the
presence of the actual locality by using a large-scale hybrid
Planet Lab-Internet measurement. We find in which even
just in the Autonomous Systems locality mechanisms with
regard to individual torrents is just not significantly
inefficient. Whenever we arrived at multiple torrent
behavior, this effectively amplifies the possibilities
associated with local sharing. We build a detection which
usually accomplishes a importance effectiveness without the
communication overhead. Because of the implementation
associated with certain pattern associated with correlation
we observe that detection rate is actually gradually
increasing. Through the simulation we now have shown
which our framework can easily successfully reduce the
cross-ISP traffic and also minimize the particular possible
degradation associated with peers’ download speed.
Published In : IJCAT Journal Volume 2, Issue 9
Date of Publication : September 2015
Pages : 372 - 375
Tables : --
Publication Link :Distribution of Peer Locality for Enhanced Sharing
in Large Scale Systems
Navuluri SaiKumar : B.Tech 4th Year, Department of CSE, SVEC, Tirupati, AP, India
Chivukula Sai kiran : B.Tech 4th Year, Department of CSE, SVEC, Tirupati, AP, India
M. Sunil Kumar : Assistant Professor (SL), Department of CSE, SVEC, Tirupati, AP, India
In this paper, we for the first time investigated the
existence and distribution o f peer locality across different
ASes through a large-scale hybrid PlanetLab Internet
measurement. We found that the BitTorrent peers do
exhibit strong geographical locality. However, the
effectiveness of a locality mechanism can be quite limited
when focusing on individual torrents, given that very few
torrents are able to form large enough local clusters.
 K. Sripanidkulchai, B. Maggs, and H. Zhang,
“Efficient Content Location Using Interest-Based
Locality in Peer-to-Peer Systems,” Proc. IEEE
 Stoica, R. Morris, D. Liben-Nowell, D.R. Karger,M.F.
Kaashoek,F.Dabek, and H. Balakrishnan, “Chord: A
Scalable Peer-to-Peer Lookup Protocol for Internet
 Applications,” IEEE/ACM Trans.Networking, vol.
11,no. 1, pp. 17-32, 2003.
 S.Tewari and L. Kleinrock, “Optimal Search
Performance in Unstructured Peer-to-Peer Networks
with Clustered Demands,”IEEE J. Selected Areas in
Comm., vol. 25, no. 1, 2007.
 B. Yang and H. Garcia-Monlina, “Efficient Search in
Peer-to-Peer Networks,” Proc. 22nd IEEE Int’l Conf.
Distributed Computing Systems (ICDCS), 2002.
 V. Cholvi, P.A. Felber, and E.W. Biersack, “Efficient
Search in Unstructured Peer-to-Peer Networks,”
European Trans.Telecomm,vol. 15, no. 6, 2004.
 E. Cohen, A. Fiat, and H. Kaplan, “Associative Search
in Peer-to-Peer Networks: Harnessing Latent
Semantics,” Proc. IEEE INFOCOM, 2003.
 M. Jelasity and O. Babaoglu. T-Man: Gossip-based
overlay topology management. In Proceedings of
Engineering Self-Organising Applications (ESOA’05),
 M. Jelasity, S. Voulgaris, R. Guerraoui, A.-M.
Kermarrec, and M. van Steen. Gossip-based peer
sampling. ACM Trans.Comput. Syst., 25(3):8, 2007.
 D. R. Karger and M. Ruhl. Simple efficient load
balancing algorithms for peer-to-peer systems. In
ACM Symposium on Parallelism in Algorithms and
Architectures (SPAA ’04),2004.
 R. Chand and P. A. Felber. Semantic peer-to-peer
overlays for publish/subscribe networks. In
Proceedings of Europar’05, European Conference on
Parallel Processing, pages 1194–1204, Lisboa,
Portugal, Sept. 2005.
 D. Cheng, R. Kannan, S. Vempala, and G. Wang. A
divide-and-merge methodology for clustering. ACM
Trans. Database Syst., 31(4):1499–1525, 2006.
 P. Ferragina and A. Gulli. A personalized search
engine based on web-snippet hierarchical clustering.
Softw. Pract. Exper.,38(2):189–225, 2008.