The document describes a proposed Socially-Aware Caching Strategy (SACS) for Content Centric Networks (CCN). SACS aims to improve caching performance by proactively caching content requested by influential users, as determined through centrality measures applied to social network data. The strategy is evaluated through simulations using real social network topologies and CCN network topologies. Results show that leveraging social network information can improve caching hit ratios compared to default CCN caching strategies.

1. Caching Strategies based on Popularity for
Content Centric Networking
C´esar Bernardini
Universit´e de Lorraine
LORIA CNRS UMI7503
INRIA Nancy – Grand Est
Jury
Reviewers: Toufik AHMED Prof. Universit´e Bordeaux 1
Dario ROSSI Prof. TELECOM ParisTech
Examiners: Guy LEDUC Prof. Universit´e de Li`ege
Olivier PERRIN Prof. Universit´e de Lorraine
Directors: Olivier FESTOR Prof. Universit´e de Lorraine
Thomas SILVERSTON Assoc. Prof. Universit´e de Lorraine

2. Motivation 1/3
The Internet
Internet was conceived in a different context [LCC+09]
Limited number of qualified users
Expensive and limited number of devices
Internet uses a host-to-host paradigm
Evolution of the Internet brought many changes &
solutions
The TCP/IP stack, The Web was created
Content Delivery solutions (i.e. CDN, P2P)
Users do not care about localization of content
Internet is mostly used to access content
Forecast: 2016, 86% of global consumer traffic [Cis12]
The communication paradigm remains the same: host-to-host

3. Motivation 2/3
The Era of Digital Video
Today
In the USA, 15 minutes of video are watched daily
66% of the total traffic
16 GB per month and per user [Nie11]
Smartphones, tablets and Smart TVs
Mobile video will increase 14-fold between
2013-2018 [Cis14]
Netflix and Youtube offer an unlimited selection of video
Emergence of High Definition technologies
Congestion everywhere
The 15 min. of video will become hours of consumption.
The 16 GB will become 600 GB of traffic [qwi].
3

4. Motivation 3/3
Summary
Content Delivery Internet
Caching features
Users are mostly interested in content rather than its
location
Information must be in the center of the scene
Information Centric Networks appear as a solution

5. Outline
1 Motivation
Content Centric Networks
CCN Cache Management
2 Comparison of Caching Strategies
Common Evaluation Scenario
Simulations & Results
3 MPC: Most Popular Content Caching strategy
MPC
Simulations & Results
4 SACS: Socially-Aware Caching strategy
SACS
Simulation & Results
5 Conclusions & Future Work
5

6. Information Centric Networks
Information Centric Networks (ICN)
Clean-design of the Internet
Content is at the center of the scene
Caching capabilities at every node
Caching capabilities at networking level
ICN architectures
Content Centric Networks [JST+
]
FP-7 PURSUIT [FNTP10]
SAIL NetInf [Dan09]
6

7. ICN: Content Centric Networks (CCN)
Primitives
Interest: user requests content issuing a named Interest
Data: every user having the answer, issue a response
Features
Every node has in-caching network support
Packets adress names, not its location
Multicast, Encryption, Signatures features

8. CCN Overview
Host 1 wants /content/abc.flv
Host 1 issues an interest message
8

9. CCN Overview: HIT
CCN Node has content /content/abc.flv in the cache
CCN Node issues a Data message with the answer
9

10. CCN Overview: MISS
CCN Node has NOT content /content/abc.flv in the cache
CCN Node retransmit the message to the rest of the
network
10

11. CCN Overview: EVICTION
/content/abc.flv content is found
A Data message is received with /content/abc.flv
CCN Node make room by evicting some content
CCN Node sends the Data with /content/abc.flv
Host 1 receives the content
11

12. CCN Cache Management
Cache Management
Content Management is the masterpiece on CCN networks
Replacement Policies
Decide what element to replace
Well-studied into OS, web-servers (LRU, FIFO, MFU, Rand,
etc.)
Replacement policies will have similar performances in the
long term [RMK13].
Caching Strategy
Decide what content to cache and its location
Challenge
Design of CCN caching strategies

13. Outline
1 Motivation
Content Centric Networks
CCN Cache Management
2 Comparison of Caching Strategies
Common Evaluation Scenario
Simulations & Results
3 MPC: Most Popular Content Caching strategy
MPC
Simulations & Results
4 SACS: Socially-Aware Caching strategy
SACS
Simulation & Results
5 Conclusions & Future Work
13

14. Comparison of Caching Strategies 1/2
Caching Strategies
Leave Copy Everywhere [JST+]
Every data message, leaves a copy in a cache
Default CCN caching strategy
Leave Copy Down [ZLL13]
After a hit, creates copy in the immediate neighbor
Cache “Less” For More [CHPP13]
Uses topological information, to decide where to cache
ProbCache [PCP12]
Probabilistic selection of caches w.r.t the distance
MAGIC [RQW+14]
Maximizes insertion of new elements
Minimizes replaced elements
14

15. Comparison of Caching Strategies 2/2
Limitations
Impossible to determine the best caching strategy for every
case
Different simulators generate different results [TRBG14]
Our approach
Evaluate all the caching strategies in a Common
Framework
Decides pros/cons and best cases for every strategy
15

16. Survey of Simulation Environments
Parameters of Comparison
Topology: interconnection between network nodes.
Content Popularity Model: function that establishes the
popularity of every piece of content.
Catalog: collection of elements in the network.
Cache Size: space available for temporal storing in every
node of the network.
16

17. Survey of Simulation Environments
Parameter values in every Simulation Environment
0
1
2
3
4
5
6
7
8
Tree
ISP
Level
Torus
RocketFuel
Frequency
Topology
(a) Topologies.
0
1
2
3
4
5
6
7
8
0.0 - 0.6
0.61 - 0.8
0.81 - 1.0
1.01 - 1.2
1.21 - 1.4
1.41 - 1.6
1.61 - 1.8
1.81 - 2.0
2.01 - 2.5
Frequency
Zipf α configuration
(b) Pop. Model
0
1
2
3
4
5
6
7
8
1 10 100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Unknown
Frequency
Catalog (number of pieces of content)
(c) Catalog.
0
1
2
3
4
5
6
7
8
1 0.1 0.01
0.001
0.0001
0.00001
Frequency
Ratio of Cache Size and Catalog size
(d) Cache Size.
17

18. Survey of Simulation Environments
Parameter values in every Simulation Environment
0
1
2
3
4
5
6
7
8
Tree
ISP
Level
Torus
RocketFuel
Frequency
Topology
(a) Topologies.
0
1
2
3
4
5
6
7
8
0.0 - 0.6
0.61 - 0.8
0.81 - 1.0
1.01 - 1.2
1.21 - 1.4
1.41 - 1.6
1.61 - 1.8
1.81 - 2.0
2.01 - 2.5
Frequency
Zipf α configuration
(b) Pop. Model
0
1
2
3
4
5
6
7
8
1 10 100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Unknown
Frequency
Catalog (number of pieces of content)
(c) Catalog.
0
1
2
3
4
5
6
7
8
1 0.1 0.01
0.001
0.0001
0.00001
Frequency
Ratio of Cache Size and Catalog size
(d) Cache Size.
Wide variety of parameters ⇒ impossible to cross-compare
17

19. Common Evaluation Scenario
Configuration
Abilene Geant DTelecom Tiger
Catalog
Catalog 106
Popularity Model MZipf(α = {0.65; 1.1; 1.5; 2.0}, β = 0)
Network
Topologies Geant, Dtelecom, Abilene, Tiger
CCN Cache Configuration
Cache size ratio {10−6; 10−5; 10−4; 10−3}
Table: Common Evaluation Scenario.
18

20. Common Framework
Metrics
Metrics
Cache Hit: total number of hits divided by the total sum of
hits and miss in the network.
Diversity: number of distinct stored elements divided by
the total number of space in the caches.
19

21. Evaluation
0.0
0.2
0.4
0.6
0.8
1.0
10-6
10-5
10-4
10-3
CacheHit
Popularity model (α = 0.65)
LCE
ProbCache
Cache Less For More
MAGIC
LCD
10-6
10-5
10-4
10-3
Popularity model (α = 1.1)
10-6
10-5
10-4
10-3
Popularity model (α = 1.5)
10-6
10-5
10-4
10-3
0.0
0.2
0.4
0.6
0.8
1.0
CacheHit
Popularity model (α = 2.0)
0.0
0.2
0.4
0.6
0.8
1.0
10-6
10-5
10-4
10-3
Stretch
10-6
10-5
10-4
10-3
10-6
10-5
10-4
10-3
10-6
10-5
10-4
10-3
0.0
0.2
0.4
0.6
0.8
1.0
Stretch
0.0
0.2
0.4
0.6
0.8
1.0
10-6
10-5
10-4
10-3
Diversity
Cache Size
10-6
10-5
10-4
10-3
Cache Size
10-6
10-5
10-4
10-3
Cache Size
10-6
10-5
10-4
10-3
0.0
0.2
0.4
0.6
0.8
1.0
Diversity
Cache Size
Figure: Comparison of the Caching Strategies
20

22. Evaluation
Popularity Model MZipf(α = {1.1). Cache Hit
0
0.2
0.4
0.6
0.8
1
10-6
10-5
10-4
10-3
CacheHit
Cache Size Ratio
LCE
ProbCache
Cache Less For More
MAGIC
LCD
21

23. Evaluation
Popularity Model MZipf(α = {1.1). Diversity
0
0.2
0.4
0.6
0.8
1
10-6
10-5
10-4
10-3
Diversity
Cache Size Ratio
LCE
ProbCache
Cache Less For More
MAGIC
LCD
22

24. Evaluation
Popularity Model MZipf(α = {1.1). Diversity
0
0.2
0.4
0.6
0.8
1
10-6
10-5
10-4
10-3
Diversity
Cache Size Ratio
LCE
ProbCache
Cache Less For More
MAGIC
LCD
High Diversity
Low Diversity
22

25. Summary
Summary
Comparison of Caching Strategies of the literature
[Submitted to Globecom 2015]
Common Framework
Results depend on the objective of the CCN network
High Diversity: LCD or Cache “Less For More”
Low Diversity: LCE or ProbCache
Better caching strategies can be deployed based on
concepts used in the caching strategies
Popularity count of MAGIC
Replication of content of LCD
23

26. Outline
1 Motivation
Content Centric Networks
CCN Cache Management
2 Comparison of Caching Strategies
Common Evaluation Scenario
Simulations & Results
3 MPC: Most Popular Content Caching strategy
MPC
Simulations & Results
4 SACS: Socially-Aware Caching strategy
SACS
Simulation & Results
5 Conclusions & Future Work
24

27. MPC: Most Popular Caching Strategy
Feasibility of Caches everywhere
Overloading resources.
Impact of traffic mix on caching performance in a
content-centric network [FRRS12]
Cache Less for More in Information Centric
Networks [CHPP13]
Our approach
Design caching strategy adapted to CCN
Cache less and smartly
25

28. MPC: Most Popular Content
Features
Cache only popular content
Simple - No prior knowledge of access patterns
Fully decentralized algorithm
How-To
Count locally number of access counts for every content
name
Information stored in a Popularity Table
Content requested >= Popularity Threshold = Popular
Once Content is marked as Popular, it is distributed
through network.
26

29. MPC: Case Study
27

30. MPC: Case Study
28

31. MPC: Case Study
29

32. Simulation Environment
ccnSim
Scalable chunk-level simulator of CCN [CRR13]
Platform to deploy easily caching strategies
Simulation Scenario
Popularity Content Model (Zipf)
Several topologies
Evaluated w.r.t. LCE (default CCN caching strategy)
30

33. MPC: Results
0.2
0.4
0.6
0.8
1
180 360 720 1140 2880 5760 11520 20000 40000
CacheHitRatio
Simulation Time Units
LCE
MPC
31

34. MPC: Results
Popularity Model MZipf(α = {1.5). Cache Hit
0.6
0.8
1
Tree Abilene Tiger2 GeantDTelekomLevel3
CacheHitRatio
Topologies
LCE
MPC
32

35. MPC: Results
Popularity Model MZipf(α = {1.5). Ratio of Cached Elements
0
0.5
1
Tree Abilene Tiger2 Geant DTelecom Level3
RatioofCachedElements
Topologies
LCE
MPC
33

36. Summary
MPC: Most Popular Content Caching Strategy
MPC caches only popular content
MPC improves Cache Hit Ratio
MPC performs less caching operations
Caching strategies should consider users of the network
Publications
Publications: [IFIP AIMS 2013], [IEE ICC 2013]
34

37. Outline
1 Motivation
Content Centric Networks
CCN Cache Management
2 Comparison of Caching Strategies
Common Evaluation Scenario
Simulations & Results
3 MPC: Most Popular Content Caching strategy
MPC
Simulations & Results
4 SACS: Socially-Aware Caching strategy
SACS
Simulation & Results
5 Conclusions & Future Work
35

38. SACS: Socially-Aware Caching Strategy
The Importance of Social Networks
OSN such as Facebook count already for one billion users.
Internet services offer new social features (i.e. Youtube,
Flickr).
People exchange their experiences in OSN (events, news).
36

39. SACS: Socially-Aware Caching Strategy
The Importance of Social Networks
OSN such as Facebook count already for one billion users.
Internet services offer new social features (i.e. Youtube,
Flickr).
People exchange their experiences in OSN (events, news).
Intuition
Caching Strategy for CCN based on Social Network
Information
36

40. Social Network Model
37

41. Social Network Model
38

42. Social Network Model
39

43. SACS: Socially-Aware Caching Strategy
Influential Users
Most important users in the Social Network.
We compute importance of users with Eigenvector &
PageRank centrality measures over the social network.
SACS
Pro-active caching of influential users’ content.
Non-Influential users’ content follow CCN normal behavior.
40

44. Example
41

45. Example
42

46. Example
43

47. Example
44

48. Example
45

49. Simulation Environment
Simulation Environment
Social Network Topology: LastFM & Facebook
CCN Topology: 3,037 nodes (inet-3.0 generator [WJ02]
Caching configuration
Replacement Policies: LRU
Centrality measure: PageRank, Eigenvector
Cache Size: 1..20
Metrics: Cache Hit, Stretch, Diversity
46

50. SACS Evaluation
0
0.2
0.4
0.6
0.8
1
1 5 10 15 20
CacheHitRatio
Cache Size
CCN (Leave Copy Everywhere)
SACS/Eigenvector
SACS/Pagerank
47

51. PlanetLab Experiments 1/2
Environment
Testbed platform for planet-scale experiments
14 nodes distributed across the globe
Social network traces [BSF]
Facebook social connections
CCNx prototype (official CCN propotype)
48

52. PlanetLab Experiments 2/2
0
0.2
0.4
0.6
0.8
1
1 5 10 15 20
CacheHitRatio
Cache Size
CCN (Leave Copy Everywhere)
SACS/PageRank
49

53. Summary
Summary
SACS is a caching strategy for CCN
SACS uses social information
SACS privileges Influential users in the network by
pro-actively caching the content they produce
SACS improves CCN performance
SACS improves Cache Hit Ratio by 2.5 times
Evaluated in the PlanetLab platform
Publications
Publications: [IFIP Networking 2014], [IEEE ICC 2014],
[IEEE IWCMC 2014]
50

54. Outline
1 Motivation
Content Centric Networks
CCN Cache Management
2 Comparison of Caching Strategies
Common Evaluation Scenario
Simulations & Results
3 MPC: Most Popular Content Caching strategy
MPC
Simulations & Results
4 SACS: Socially-Aware Caching strategy
SACS
Simulation & Results
5 Conclusions & Future Work
51

55. General Conclusions
Contributions
Conclusions
Future Internet could rely on CCN – network of caches
Design/evaluate caching strategies
Comparison of the caching strategies
Common Framework
Results depend on the objective of the CCN network
Most Popular Caching Strategy (MPC)
MPC caches only popular content
Improves performance of CCN
Socially Aware Caching Strategy
SACS caches content from popular users
Improves performance of CCN
52

56. General Conclusions
Perspectives
Perspectives
Compare against current Internet solutions for content
delivery (CDN, P2P)
Social-Routing for CCN
Virtualization of CCN
Security problems of the CCN architecture
53

57. Questions
54

58. References I
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Conference on Communications (ICC) 2013, IEEE, 2013,
pp. 2309–2314.
Christian Dannewitz, NetInf: An Information-Centric design for the future
internet.
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59. References II
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Nielsen Media Research, Nielsen cross-platform report q3, Nielsen
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56

60. References III
Ioannis Psaras, Wei Koong Chai, and George Pavlou, Probabilistic
in-network caching for information-centric networks, Proceedings of the
Second Edition of the ICN Workshop on Information-centric Networking
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61. References IV
Michele Tortelli, Dario Rossi, Gennaro Boggia, and Luigi Alfredo Grieco,
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