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)
![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
On a novel joint replicating and caching strategy for
content centric networks
Portal Device with caching
ABSTRACT
support (e.g. Router,
DSLAM, )
STB with possible
caching support
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.
CDN The CCN paradigm is considered as one of the most
surrogate
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.
Fig.1 Intra-‐Domain Architecture
Fig.2 Popularity vs. Number of Nodes
Number of Nodes
Content replication vs. caching in CCN 18
16
Early, in-network caching was proposed as a mean to get the 14
contents closer to the end-users. With the shift towards 12
content-centric networking (CCN), this logic is pushed further.
10
CCN introduces two distinct techniques: contents caching and
8
replication. However, one should consider the mutual impact
existing between these techniques. Indeed, the benefits of 6
contents replication can be completely cancelled with a 4
bad caching technique (see Fig. 3). 2
Popularity
0
0 5E-‐11 1E-‐10 1,5E-‐10 2E-‐10 2,5E-‐10
Fig.3 Popularity vs. Delay
0,8
Limits of existing approaches
Delay
0,7
0,6
CCNs allow popular content to be present in many nodes to make
0,5 the content closer to the end users (see Fig. 2). However, the
0,4 use of LRU as a caching strategy deceases the duration of
0,3 the contents presence in caches.
0,2 Some changes should be introduced to the classical CCN
0,1 architecture by focusing on: (i) reducing the amount of
0 replica in the intra-domain; (ii) storing as many various data
Popularity
0 5E-‐11 1E-‐10 1,5E-‐10 2E-‐10 2,5E-‐10 as possible.
A Combined caching and replication Each CCN node, in the path to the destination, stores the chunk
using this probability.
technique
The proposed caching technique, combines the benefits of
Initially affect to each piece of data (i.e. chunk), to be Least Recently Used (LRU) and Least Frequently Used (LFU)
transmitted, a nonzero storage probability depending notably on solutions.
the chunk s popularity P.
If a CCN node decides to store a particular chunk, it puts
P0 = min [ max (( i) , 0) , 1]
P= its storage probability to zero (or reduce the probability) to
PK = min[max(PK-1 + S /N , 0) , 1]
where i : popularity of the chunk i. S = 1 if the chunk have been avoid multiple duplication of the same content. Otherwise,
stocked, 0 else. the probability is increased.
Acknowledgement The work is partially supported by the national French project ANR VERSO ViPeer.](https://image.slidesharecdn.com/5ghazzai-130118131540-phpapp02/85/CCNxCon2012-Poster-Session-On-a-Novel-Joint-Replicating-and-Caching-Strategy-for-Content-Centric-Networks-1-320.jpg)
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CCNxCon2012: Poster Session:On a Novel Joint Replicating and Caching Strategy for Content-Centric Networks
- 1. 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 On a novel joint replicating and caching strategy for content centric networks Portal Device with caching ABSTRACT support (e.g. Router, DSLAM, ) STB with possible caching support 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. CDN The CCN paradigm is considered as one of the most surrogate 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. Fig.1 Intra-‐Domain Architecture Fig.2 Popularity vs. Number of Nodes Number of Nodes Content replication vs. caching in CCN 18 16 Early, in-network caching was proposed as a mean to get the 14 contents closer to the end-users. With the shift towards 12 content-centric networking (CCN), this logic is pushed further. 10 CCN introduces two distinct techniques: contents caching and 8 replication. However, one should consider the mutual impact existing between these techniques. Indeed, the benefits of 6 contents replication can be completely cancelled with a 4 bad caching technique (see Fig. 3). 2 Popularity 0 0 5E-‐11 1E-‐10 1,5E-‐10 2E-‐10 2,5E-‐10 Fig.3 Popularity vs. Delay 0,8 Limits of existing approaches Delay 0,7 0,6 CCNs allow popular content to be present in many nodes to make 0,5 the content closer to the end users (see Fig. 2). However, the 0,4 use of LRU as a caching strategy deceases the duration of 0,3 the contents presence in caches. 0,2 Some changes should be introduced to the classical CCN 0,1 architecture by focusing on: (i) reducing the amount of 0 replica in the intra-domain; (ii) storing as many various data Popularity 0 5E-‐11 1E-‐10 1,5E-‐10 2E-‐10 2,5E-‐10 as possible. A Combined caching and replication Each CCN node, in the path to the destination, stores the chunk using this probability. technique The proposed caching technique, combines the benefits of Initially affect to each piece of data (i.e. chunk), to be Least Recently Used (LRU) and Least Frequently Used (LFU) transmitted, a nonzero storage probability depending notably on solutions. the chunk s popularity P. If a CCN node decides to store a particular chunk, it puts P0 = min [ max (( i) , 0) , 1] P= its storage probability to zero (or reduce the probability) to PK = min[max(PK-1 + S /N , 0) , 1] where i : popularity of the chunk i. S = 1 if the chunk have been avoid multiple duplication of the same content. Otherwise, stocked, 0 else. the probability is increased. Acknowledgement The work is partially supported by the national French project ANR VERSO ViPeer.