1. Kenza Hamidouche, Mérouane Debbah
Many-to-Many Matching Games for Proactive Social Caching in Wireless Small Cell Network
International Workshop on Wireless Networks: Communication, Cooperation and Competition (WNC3), 2014
Kenza Hamidouche, Mérouane Debbah
Alcatel-Lucent Chair on Flexible Radio - SUP´ ELEC, Gif-sur-Yvette, France
Walid Saad Department of Electric and Computer Engineering, University of Miami
Speaker: Yi-Ting Chen

2. Outline Introduction System Model Proposed Method and Algorithm
Simulation Result and Analysis
Conclusions

3. Introduction
To ensure acceptable Quality of Experience (QoE) for the end-users
a very dense deployment of low-cost and low-power small base stations (SBSs)

4. Introduction(cont.)
However, the prospective performance gains will be limited by capacity limited and possibly heterogeneous backhaul links that connect the SBSs to the core network [3].

5. Introduction(cont.)
Distributed caching at the network edge is considered as a promising solution to deal with the backhaul bottleneck.
Basic Idea:
Duplicate and store the data at the SBSs side

6. Prior Works
The cache placement problem has been mainly addressed for wired networks
Especially for Content Delivery Networks (CDNs) [7]–[10].

7. Prior Works(cont.)
The placement problem in wireless networks has been studied in [5], [6], [11]
Minimizes the expected delay for data recovery has been proposed
Defined without considering the limited capacity of backhaul links
Assignment of data is based on the global popularity of videos

8. Reference
[5] N. Golrezaei, K. Shanmugam, A. G. Dimakis, A. F. Molisch, and G. Caire, “Femtocaching: Wireless video content delivery through distributed caching helpers,” in Proc. of IEEE International Conference on Computer Communications, Orlando, FL, USA, Mar. 2012, pp. 1107– [6] N. Golrezaei, A. G. Dimakis, and A. F. Molisch, “Wireless deviceto-device communications with distributed caching,” in Proc. of IEEE International Symposium on Information Theory, Cambridge, MA, USA, Jul. 2012, pp. 2781–2685. [7] S. Borst, V. Gupta, and A. Walid, “Distributed caching algorithms for content distribution networks,” in Proc. of IEEE International Conference on Computer Communications, San Diego, CA, USA, Mar. 2010, pp. 1–9. [8] L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker, “Web caching and zipf-like distributions: Evidence and implications,” in Proc. of IEEE International Conference on Computer Communications, New York, NY, USA, Mar. 1999, pp. 126–134.

9. Reference
[9] M. M. Amble, P. Parag, S. Shakkottai, and Y. Lei, “Content-aware caching and traffic management in content distribution networks,” in Proc. of IEEE International Conference on Computer Communications, Shanghai, China, Apr. 2011, pp – 2866.
[10] I. Joe, J. H. Yi, and K.-S. Sohn, “A content-based caching algorithm for streaming media cache servers in cdn,” Multimedia, Computer Graphics and Broadcasting Communications in Computer and Information Science, vol. 262, pp. 28–36, 2012.
[11] N. Golrezaei, K. Shanmugam, A. G. Dimakis, A. F. Molisch, and G. Caire, “Wireless video content delivery through coded distributed caching,” in Proc. of IEEE International Conference on Communications, Pacific Grove, CA, USA, Jun

10. Main Contributions Developing a novel caching algorithm
Reduce the backhaul load and the experienced delay by the end-users when accessing shared videos in Online Social Networks (OSNs)

11. System Model (Service Provider Servers)
Video V={ 𝒗 𝟏 , 𝒗 𝟐 , 𝒗 𝟑 ,…, 𝒗 𝑽 }
(Small Base Stations)
(User Equipments)
Capacities: Q={ 𝒒 𝟏 , 𝒒 𝟐 , 𝒒 𝟑 ,…, 𝒒 𝑴 }

12. Goal
To produce a proactive download of video content at the SBSs level

13. Method Predicting users’ requests to select and cache videos
By three factors : 𝐼 Social , 𝐼 Sharing , 𝐼 Interests
Hence, we formulate this caching problem as a many-to-many matching game

14. Proposed Algorithm – Phase1
Phase1: Network Discovery
SPSs and SBSs discover their neighbors
Collecting the required parameters to define the preferences

15. Preference of the Small Base Stations
Social Interactions: 𝐼 Social
A user is more likely to request a video, shared by one of his friends
Users’ Interests: 𝐼 Interests
User can request a video with his interested topic irrespective of the friend who shared it
Sharing Impact: 𝐼 Sharing = 𝐹 𝑙 𝑚 ． 𝑆 𝑔𝑙 𝑖=1 𝐺 𝑆 𝑖𝑙
If a video is cached in the SBS, sharing with the user’s friends can have an important impact on the traffic load

16. Preference of the Small Base Stations
We define the local popularity of a video 𝑣 𝑖 at the 𝑚 𝑡ℎ SBS as follows:
𝑃 𝑣 𝑖 = 𝑖=1 𝐹 𝑛 𝑚 𝐼 𝑆ℎ𝑎𝑟𝑖𝑛𝑔 𝛾 ． 𝐼 𝑆𝑜𝑐𝑖𝑎𝑙 +(1−𝛾) 𝐼 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑠
,𝛾=[0,1]

17. Preferences of the Service Provider Servers
SPS would prefer to cache a video at the SBS
Offers the smallest download time for the expected requesting UEs.
The download time:
𝑇 𝐷 = 1 𝑚𝑖𝑛 𝑏 𝑖𝑗 , 𝑛=1 𝑁 𝑟 𝑗𝑛 𝑁

18. Proposed Algorithm – Phase2
SPSs define a preference list for each owned file over the set of SBSs
SBSs define their preferences over the set of videos proposed by the SPSs.

19. Proposed Algorithm – Phase3
First Step:
Every SPS proposes an owned video to the most preferred set of SBSs (shortest download time)
Each SBS rejects all but the most preferred videos
Second Step:
Every SPS proposes an owned video to the most preferred set of SBSs which have not yet rejected it
Each SBS rejects all the most preferred videos
Repeat the Second Step
Until Convergence to a stable matching

20. 𝑪 𝒗 𝟏 (𝑴, 𝟏) = {s4, s3} 𝑪 𝒗 𝟏 (𝑴, 𝟐) = {s4, s3}
𝒗 𝟖 , 𝒗 𝟗 , 𝒗 𝟏𝟎
𝒗 𝟓 , 𝒗 𝟔 , 𝒗 𝟕
𝒗 𝟏 , 𝒗 𝟐
𝒗 𝟏 , 𝒗 𝟐
𝒗 𝟑 , 𝒗 𝟒
𝑪 𝒗 𝟏 (𝑴, 𝟏) = {s4, s3}
𝑪 𝒗 𝟏 (𝑴, 𝟐) = {s4, s3}
𝑪 𝒗 𝟑 (𝑴, 𝟏) = {s4, s3}
𝑪 𝒗 𝟑 (𝑴, 𝟐) = {s3}
𝒗 𝟑 , 𝒗 𝟒
𝑷 𝑺 𝟒 (𝟏) = { 𝒗 𝟏 , 𝒗 𝟐 }

21. Pairwise Stability Proposition 1: Offers remain open
For every video 𝑣 𝑖 , if an SBS 𝑠 𝑗 is contained in 𝐶 𝑣 𝑖 (𝑀, (𝑘−1)) at step k − 1 and did not reject 𝑣 𝑖
then 𝑠 𝑗 is contained in 𝐶 𝑣 𝑖 (𝑀, 𝑘)
Proposition 2: Rejections are final
If a video 𝑣 𝑖 is rejected by an SBS 𝑠 𝑗 at step k
Then at any step p ≥ k, 𝑣 𝑖 ∉ 𝐶 𝑆 𝑗 (𝑃 𝑆 𝑗 𝑝 ∪{ 𝑣 𝑖 })

22. Pairwise Stability
Theorem. The proposed matching algorithm between SPSs and SBSs is guaranteed to converge to a pairwise stable matching.

23. Simulation Result and Analysis
Setting:
Video V=100
K=80
B=80 Mbit/time
M=150
N=400
R=180 Mbit/time

24. Simulation Result and Analysis
Compare the two algorithm:
The proposed matching algorithm (MA)
Random caching algorithm (RA)
We compare with different values of a storage ratio β
represents the number of files that each SBS has the capacity to store

25. Simulation Result and Analysis
𝒔𝒂𝒕𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝒓𝒂𝒕𝒊𝒐= 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒔𝒆𝒓𝒗𝒆𝒅 𝒓𝒆𝒒𝒖𝒆𝒔𝒕𝒔 𝒃𝒚 𝒕𝒉𝒆 𝑺𝑩𝑺𝒔 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒂𝒍𝒍 𝒓𝒆𝒒𝒖𝒆𝒔𝒕𝒔
Simulation Result and Analysis

26. Simulation Result and Analysis

27. Conclusion This paper proposed a novel caching approach
The method overcoming the backhaul capacity constraints in wireless small cell networks
Simulation results have shown that the proposed matching game reduces the backhaul links load as well as the experienced delay by the end-users

28. Thanks for your listening!