Social and Spatial Proactive Caching for Mobile Data Offloading IEEE International Conference on Communications (ICC) – W3: Workshop on Small Cell and 5G Networks, 2014 Ejder Bastug and Merouane Debbah Alcatel-Lucent Chair - SUPÉLEC, France Mehdi Bennis Centre for Wireless Communications, University of Oulu, Finland Speaker: Yi-Ting Chen This research has been supported by the ERC Starting Grant MORE (Advanced Mathematical Tools for Complex Network Engineering) and the SHARING project under the Finland grant

Outline Introduction System Model Problem Formulation Backhaul Offloading via Proactive Caching –Evaluation and Discussion Social-Aware Caching via D2D –Evaluation and Discussion Conclusions 2

Introduction 3 Currently, mobile video streaming accounts for 50% of mobile data traffic. And it is expected to have a 500-fold increase over the next ten years [2]. Online social networking (Facebook, Twitter, Digg, etc.) is the second largest contributor to this traffic with a 15% average share [3]. [2] Cisco, “Cisco visual networking index: Global mobile data traffic forecast update, ,” White Paper, [Online] [3] Ericsson, “5g radio access - research and vision,” White Paper, [Online]

Introduction One way of taming these unrelenting demands is via the deployment of small cell networks (SCNs). The gist of SCN studies revolve around: –Self-organization –Inter-cell interference coordination (ICIC), –Traffic offloading [6], –Energy efficiency [5] 4 [5] J. Andrews, “Seven ways that hetnets are a cellular paradigm shift,” IEEE Communications Magazine, vol. 51, no. 3, pp. 136–144, 2013.

Introduction In order to cater for peak traffic demands expensive high-speed backhaul deployments are required. A novel networking paradigm taking into account –Recent advances in storage –Context-awareness –Social networking 5 [6] M. Bennis, M. Simsek, W. Saad, S. Valentin, M. Debbah, and A. Czylwik, “When cellular meets wifi in wireless small cell networks,” IEEE Communication Magazine, Special Issue in HetNets, June 2013.

Main Contribution We propose a proactive caching framework. By exploiting the predictability of future demands, popular contents are proactively cached before users actually request them. Further, whenever D2D communication is possible, the proposed caching approach exploits users’ social ties, physical proximity and users’ storage for content dissemination. 6

System Model 7

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Problem Formulation 11

Problem Formulation 12

Backhaul Offloading via Proactive Caching 13

Backhaul Offloading via Proactive Caching 14

Caching Procedure 15

Caching Procedure 16

Caching Procedure 17

Numerical Results -- Parameter 18

Numerical Results Impact of number of requests: 19

Numerical Results Impact of cache size: 20

Numerical Results Impact of popularity distribution: 21

Social-Aware Caching via D2D 22

Social-Aware Caching via D2D 23

Social-Aware Caching via D2D The set of influential users needs to be identified. –By exploiting the social relationships among users via the notion of centrality metric [15]. The centrality metric measures the social influence of a node on how well it connects the network –A higher value means a more influential node to its social community. We use the eigenvector centrality. 24 [15] M. Newman, Networks: an introduction. Oxford University Press, 2009.

Social-Aware Caching via D2D 25

Disseminate Contents within Each Social Community 26 [16] T. L. Griffiths and Z. Ghahramani, “The indian buffet process: An introduction and review,” J. Mach. Learn. Res., vol. 12, pp. 1185–1224, Jul

Disseminate Contents within Each Social Community The selection result of user n, defined as the conjugate probability of the Beta distribution follows a Bernoulli distribution. It turns out that the resulting user-file partition is reminiscent to that of the Chinese restaurant process (CRP). 27 [16] T. L. Griffiths and Z. Ghahramani, “The indian buffet process: An introduction and review,” J. Mach. Learn. Res., vol. 12, pp. 1185–1224, Jul

Chinese restaurant process (CRP) 28 Customers → Users Tables → Files

Disseminate Contents within Each Social Community 29 [16] T. L. Griffiths and Z. Ghahramani, “The indian buffet process: An introduction and review,” J. Mach. Learn. Res., vol. 12, pp. 1185–1224, Jul

Numerical Results -- Parameter 30

Numerical Results Impact of number of requests: 31

Numerical Results Impact of D2D cache size: 32

Numerical Results Impact of CRP concentration parameter: 33

Conclusions A novel proactive networking paradigm where caching plays an important role. The proactive caching solution exploits users’ predictable demands, storage, and their social relationships to minimize peak mobile data traffic demands. It was demonstrated that precaching strategic contents at the network edge engenders significant backhaul offloading gains and resource savings. 34

Thanks for your listening! 35