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On a novel joint replicating and caching strategy for content centric networks ABSTRACT The rise of popularity of video services has resulted in increased volumes of network traffic that, in turn, has created bottlenecks in the networks causing degradations of the perceived quality. The CCN paradigm is considered as one of the most prominent solution to address such issue. However, the cascaded LRU caches introduced by CCN presents some limits. In this work, we first analyze such limits. Then, we propose a new caching and replication strategy to optimize resources utilization and to maximize the number of different chunks existing within the intra domain. L.GHAZZAI 1 Y.HADJADJ-AOUL 1 A.KSENTINI 1 G.BICHOT 2 S.GOUACHE 2 A.BELGHIT 3 1 University of Rennes 1 2 Technicolor 3 University of Manouba Content replication vs. caching in CCN Early, in-network caching was proposed as a mean to get the contents closer to the end-users. With the shift towards content-centric networking (CCN), this logic is pushed further. CCN introduces two distinct techniques: contents caching and replication. However, one should consider the mutual impact existing between these techniques. Indeed, the benefits of contentsreplication can be completely cancelled with a bad caching technique (see Fig. 3). Limits of existing approaches CCNs allow popular content to be present in many nodes to make the content closer to the end users (see Fig. 2). However, the use of LRU as a caching strategy deceases the duration of the contentsÓ presence in caches. Some changes should be introduced to the classical CCN architecture by focusing on: (i) reducing the amount of replica in the intra-domain; (ii) storing as many various data as possible. A Combined caching and replication technique Initially affect to each piece of data (i.e. chunk), to be transmitted, a nonzero storage probability depending notably on the chunks popularity P. P = where i : popularity of the chunk i. S = 1 if the chunk have been stocked, 0 else. Each CCN node, in the path to the destination, stores the chunk using this probability. The proposed caching technique, combines the benefits of Least Recently Used (LRU) and Least Frequently Used (LFU) solutions. If a CCN node decides to store a particular chunk, it puts its storage probability to zero (or reduce the probability) to avoid multiple duplication of the same content. Otherwise, the probability is increased. Acknowledgement The work is partially supported by the national French project ANR VERSO ViPeer. CDN surrogate Portal Device with caching support (e.g. Router, DSLAM, ) STB with possible caching support Fig.1 IntraDomain Architecture 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0 5E11 1E10 1,5E10 2E10 2,5E10 Fig.3 Popularity vs. Delay Delay Popularity 0 2 4 6 8 10 12 14 16 18 0 5E11 1E10 1,5E10 2E10 2,5E10 Fig.2 Popularity vs. Number of Nodes Number of Nodes Popularity P 0 = min [ max ((くさ i ) g , 0) , 1] P K = min[max(P K-1 + S/N , 0) , 1]

CCNxCon2012: Poster Session:On a Novel Joint Replicating and Caching Strategy for Content-Centric Networks

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On a Novel Joint Replicating and Caching Strategy for Content-Centric Networks Leila Ghazzai, Yassine Hadjadj-Aoul, Adlen Ksentini (IRISA Lab.), Guillaume Bichot, Stephane Gouache (Technicolor), Abdelfettah Belghit (ENSI)

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Page 1: CCNxCon2012: Poster Session:On a Novel Joint Replicating and Caching Strategy for Content-Centric Networks

On a novel joint replicating and caching strategy for content centric networks

ABSTRACT The rise of popularity of video services has resulted in increased

volumes of network traffic that, in turn, has created bottlenecks in the networks causing degradations of the perceived quality. The CCN paradigm is considered as one of the most prominent solution to address such issue. However, the cascaded LRU caches introduced by CCN presents some limits. In this work, we first analyze such limits. Then, we propose a new caching and replication strategy to optimize resources utilization and to maximize the number of different chunks existing within the intra domain.

L.GHAZZAI 1 Y.HADJADJ-AOUL 1 A.KSENTINI 1 G.BICHOT 2 S.GOUACHE 2 A.BELGHIT 3 1 University of Rennes 1 2 Technicolor 3 University of Manouba

Content replication vs. caching in CCN Early, in-network caching was proposed as a mean to get the

contents closer to the end-users. With the shift towards content-centric networking (CCN), this logic is pushed further. CCN introduces two distinct techniques: contents caching and replication. However, one should consider the mutual impact existing between these techniques. Indeed, the benefits of contents’ replication can be completely cancelled with a bad caching technique (see Fig. 3).

Limits of existing approaches

CCNs allow popular content to be present in many nodes to make the content closer to the end users (see Fig. 2). However, the use of LRU as a caching strategy deceases the duration of the contents presence in caches.

Some changes should be introduced to the classical CCN architecture by focusing on: (i) reducing the amount of replica in the intra-domain; (ii) storing as many various data as possible.

A Combined caching and replication technique

Initially affect to each piece of data (i.e. chunk), to be transmitted, a nonzero storage probability depending notably on the chunk’s popularity P.

P =

where i : popularity of the chunk i. S = 1 if the chunk have been stocked, 0 else.

Each CCN node, in the path to the destination, stores the chunk using this probability.

The proposed caching technique, combines the benefits of Least Recently Used (LRU) and Least Frequently Used (LFU) solutions.

If a CCN node decides to store a particular chunk, it puts its storage probability to zero (or reduce the probability) to avoid multiple duplication of the same content. Otherwise, the probability is increased.

Acknowledgement The work is partially supported by the national French project ANR VERSO ViPeer.

CDN  surrogate

Portal Device  with  caching  support  (e.g.  Router,  

DSLAM,   )

STB  with  possible  caching  support

Fig.1  Intra-­‐Domain  Architecture

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0 5E-­‐11 1E-­‐10 1,5E-­‐10 2E-­‐10 2,5E-­‐10

Fig.3  Popularity  vs.  DelayDelay

Popularity

0

2

4

6

8

10

12

14

16

18

0 5E-­‐11 1E-­‐10 1,5E-­‐10 2E-­‐10 2,5E-­‐10

Fig.2  Popularity  vs.  Number  of  NodesNumber of Nodes

Popularity

P0 = min [ max (( i) , 0) , 1] PK = min[max(PK-1 + S /N , 0) , 1]