Efficient Content Sharing Taking Account of Updating
Replicas in Hybrid Peer-to-Peer
Networks
Tatsuru Kato, Shinji Sugawara, Yutaka Ishibashi
Nagoya Institute of Technology
Nagoya, Japan
2011 Annual IEEE CQR International WorkshopMay 10-12,2011, The Naples Beach Hotel & Golf Club
Background
• In date sharing using Peer-to-Peer (P2P) system
• Increase of storage load in each peer• Possibility to access the obsolete information
: Peer
: Contents before the update
Up-date
Req-
uest
Problems
: Contents after the update
Objective
• In data sharing using hybrid P2P networks, replicas’ relocation strategies to reduce consumed storage resources are proposed• Content update is not considered
Actually, the content update occurs on networks
A control to keep a consistency with contents
We propose a content sharing strategy that has a consistency control responding to content updates
This study
Previous study
Formulation of the problem (1/2)
• A P2P network topology that consists of many peers and their links is given
Assumption
• A server for managing all the peers is prepared, and it is aware of what content item is stored in each peer on the network • Content updating occurs only on peers having original content items, and each original content item exists on a single peer at most in the network
Formulation of the problem (2/2)
We achieve a content sharing strategy that minimizes the weighted sum of the
costs
• Network costThe load that accrues in the network when some replicas are downloaded, transferred, replicated, and updated informationis propagated
• Storage cost
• Content loss costThe cost that accrues when a peer cannot get the requested content item because no peer possesses a replica of the con-tent item in the network
The capacity to store a replica to be shared
Proposed method
• Content Request Procedure Replication, or relocation of a replica are executed
• Content Update Procedure Propagation of the latest replica is working
Restrain storage cost by restricting unnece-ssary replication, and propagation of the latest replica
Proposed method
• Content Request Procedure
• Content Update Procedure
Replication, or relocation of a replica are executed
Propagation of the latest replica is working
Restrain storage cost by restricting unnece-ssary replication, and propagation of the latest replica
Each replica has a distance range within a hop thre-shold(Hth) from the peer in which the replica is stored. This range is called the “referable range’’ referable rangeExample : Hth = 2
Referable range
: peer
: replica
When the requesting peer does not exist within the referable range, a replica of the content is
replicated on the requesting peer from the peer possessing the
contentExample : Hth = 2
Referable range
: peer
: replica
:requesting peer
Replication
Example : Hth = 2
Referable range
: peer
: replica
:requesting peer
RelocationReference
When the requesting peer exists within the referable
range, content replication is not executed and just
referred the content from the requesting peer
Relocation of the replica (1/5)
(1)Select one of the peers inside the referable range,
and suppose that the referred replica is relocated.
On all peers inside the referable range, calculate CR .
CR is an expected value of the cost to access the
replica from the other peers.
Calculate sum of CR
: peer
: replica
: updating peer:requesting peer
Relocation of replica (2/5)
(2) Change the peer to be supposed that the referred replica is relocated, calculate sum of CR on all peers inside the referable range
: peer
: replica
: updating peer:requesting peer
Calculate sum of CR
Calculate sum of CR
Relocation of replica (3/5)
Calculate CU : peer
: replica
: updating peer:requesting peer
(3) On all peers inside the referable range, calculate CU .
CU is an expected value of the cost to update the replica.
Relocation of replica (4/5)
: peer
: replica
: updating peer:requesting peer
(4) Change the peer to be supposed that the referred
replica is relocated, calculate sum of CU on all
peers inside the referable rangeCalculate CU
Relocation of replica (5/5)
(5) Calculate C = CR +WU ・ CU
Relocate the replica to the peer which has the least C WU is a weight to adjust the importance of CR
andCU
The peer which has the least C
Relocate the replica
: peer
: replica
: updating peer:requesting peer
Proposed method
• Content Request Procedure
• Content Update Procedure
Restrain storage cost by restricting replica-tion, and propagation of the latest replica
Replication, or relocation of a replica are executed
Propagation of the latest replica is working
Propagation of the replica (1/2)
Occurrence of update
The latest replica is sent only to the peers that hold the same content item’s obsolete replicas which have not referred for more than T1 units of time
: peer
: replica
: updating peer
Propagation of the replica (2/2)
Propagation of the replica
The latest replica is sent only to the peers that hold the same content item’s obsolete replicas which have not referred for more than T1 units of time
: peer
: replica
: updating peer
Evaluation method
E : Total cost
EN : Network cost (sum of the number of hops of data movement per total elapsed time)
ES : Storage cost (sum of the number of replicas in each unit time per total elapsed time)
EL : Content loss cost (sum of the number of contents loss times per total elapsed time)
• Evaluate the methods by computer simulation
WN ,WS ,WL : Weight (the relative importance of each of costs)
E =WN ・ EN +WS ・ ES +WL ・ EL
Methods for comparison
Requested content is regularly replicated on the requesting peer
RCT
Owner replication
When the number of hops between the peer possessing the content and the requesting peer is larger than threshold Hth ,replicate the content on the requesting peer.Otherwise, the requesting peer only refers to the replica.
Simulation conditions
Request of contents ・・・ Poisson distribution(λreq =0.5)Peers joining and dropping out ・・・ Poisson distribution (λmov =0.1)
Network topology BA model
Initial number of peers
100
Total number of contents
30
Threshold Hth 1 ~ 6
ThresholdT1 200
Weight (WN, WS, WL) (2, 1, 5), (1, 2, 5)
An example of BA model
Simulation result (1/2)I :95% confidence interval
Tota
l co
st E
Total costE (WN =2,WS=1,WL =5)
Proposed(Hth=1) RCT(Hth=1)Owner
Simulation result (2/2)
Total costE (WN =1,WS=2,WL =5)
RCT(Hth=1)Owner
I :95% confidence interval
Tota
l co
st E
Proposed(Hth=2)
Conclusions
Proposed an efficient content replication method taking account of updating replicas for Hybrid P2P
• When the network cost is more expensive, the proposed method succeeded in reducing total cost as well as owner replication
• When the storage cost is more expensive, the proposed method reduced total cost more than owner replication and RCT
Future works
• Investigate effectiveness of the proposed method in much more various network environments• Improve the proposed method for further efficiency of relocation of replicas
Formulation of the problem
• Each peer can possess shared content items that can be re- plicated and downloaded by other peers
• There are many different shared content items in the net- work, and each of them are replicated and held by some peers for redundancy• Each peer (actually, a user on the peer) requests content from time to time, and this request preference is biased depending on the combination of each content item and the requesting peer
Assumption
• Each of the peers can drop off the network randomly once in a while and join in again with a certain probability
• All content items have the same size
• Storage capacity of each peer is not limited
Assumption
Formulation of the problem
1. Select a dropping out peer randomly
2. Select one of the neighboring peer of the dropping out peer randomly, change link of the others to the selected peer
Dropping out peer Selected peer
Drop out of a peer
BA (Barabasi-Albert) model topology
scale-free network
• A peer is added to the network one at a time •A peer is preferentially connected to the peer which has many neighbor peers
Zipf distribution
Zipf distribution (S=1 、 N=100)
N1n
s
s
nk
P(k)S : Zipf’s coefficient N: Total number of dates
Poisson distribution
Unit of time k
even
t pro
bab
ility
P(k
)
Poisson distribution (λ= 0.5)
!k
e k λλ-P(k)
λ: Average frequency of occurrence of an event
Con
tent
loss
cost
EL
Content loss cost EL
Simulation result
Threshold Hth
I :95% confidence interval
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