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 Workshop May 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 cost The load that accrues in the network when some replicas are downloaded, transferred, replicated, and updated information is propagated Storage cost Content loss cost The 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( H th ) from the peer in which the replica is stored. This range is called the “referable range’’ referable range Example ： H th = 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 content Example ： H th = 2 Referable range ： peer ： replica ： requesting peer Replication

Example ： H th = 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 C R. C R is an expected value of the cost to access the replica from the other peers. Calculate sum of C R ： 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 C R on all peers inside the referable range ： peer ： replica ： updating peer ： requesting peer Calculate sum of C R

Relocation of replica (3/5) Calculate C U ： peer ： replica ： updating peer ： requesting peer (3) On all peers inside the referable range, calculate C U. C U 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 C U on all peers inside the referable range Calculate C U

Relocation of replica (5/5) (5) Calculate C = C R + W U ・ C U Relocate the replica to the peer which has the least C W U is a weight to adjust the importance of C R and C U 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 T 1 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 T 1 units of time ： peer ： replica ： updating peer

Evaluation method E ： Total cost ENEN ： Network cost (sum of the number of hops of data movement per total elapsed time) ESES ： Storage cost (sum of the number of replicas in each unit time per total elapsed time) ELEL ： Content loss cost (sum of the number of contents loss times per total elapsed time) Evaluate the methods by computer simulation W N, W S,W L ： Weight (the relative importance of each of costs) E = W N ・ E N + W S ・ E S + W L ・ E L

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 H th, replicate the content on the requesting peer. Otherwise, the requesting peer only refers to the replica.

Simulation conditions Request of contents ・・・ Poisson distribution( λ req =0.5) Peers joining and dropping out ・・・ Poisson distribution ( λ mov =0.1) Network topologyBA model Initial number of peers 100 Total number of contents 30 Threshold H th 1～61～6 Threshold T Weight ( W N, W S, W L ) (2, 1, 5), (1, 2, 5) An example of BA model

Simulation result (1/2) I : 95 % confidence interval Total cost E Total cost E ( W N =2, W S =1, W L =5) Proposed( H th =1) RCT ( H th =1)Owner

Simulation result (2/2) Total cost E ( W N =1, W S =2, W L =5) RCT ( H th =1)Owner I : 95 % confidence interval Total cost E Proposed( H th =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) S : Zipf’s coefficient N: Total number of dates

Poisson distribution Unit of time k event probability P(k) Poisson distribution ( λ= 0.5 ) λ: Average frequency of occurrence of an event

Simulation result Network cost E N Threshold H th

Storage cost E S Simulation result Threshold H th I : 95 % confidence interval

Content loss cost E L Simulation result Threshold H th I : 95 % confidence interval

Simulation result (1/2) I : 95 % confidence interval Total cost E Total cost E ( W N =2, W S =1, W L =5)

Simulation result (2/2) Total cost E ( W N =1, W S =2, W L =5) I : 95 % confidence interval Total cost E