Abstract
Mobile network performance and user Quality of Experience have been negatively affected by the explosion of mobile data traffic. This paper proposes mobile edge caching to alleviate the problem. Recent research has focused on local caching at the wireless edge, as motivated by the 80/20 rule regarding content popularity. By caching popular contents at base stations (BSs) closer to users, backhaul congestion and content access latency can be dramatically reduced. To address the limited storage size of BSs in the context of the massive amount of available content, an algorithm optimizing cooperative caching has been highlighted. Contents requested by mobile users that cannot be obtained locally could be transferred by cooperative BSs. In this paper, we propose a cooperative caching algorithm based on BS content access patterns. We use tensor decompositions with distance constraint to analyze interaction between users, contents and base stations. Thus, BSs with small geographical distances and similar content access patterns constitute a cooperative caching domain. The distributed content placement and delivery algorithm is optimized based on simultaneous consideration of the caching hit ratio and cooperative cost. Simulation results based on a real dataset of usage detail records demonstrate the superior performance and promising practical gains in caching of the proposed caching method compared to user clustering and BS clustering.
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References
Cisco Visual Networking Index. (2016). Global mobile data traffic forecast update, 2012–2020, Cisco, White paper.
Ahmed, A., & Ahmed, E. (2016). A survey on mobile edge computing. In 10th IEEE international conference on intelligent systems and control, (ISCO 2016). IEEE.
Wang, S., Zhang, X., Zhang, Y., et al. (2017). A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access,5, 6757–6779.
5G Infrastructure PPP Association. 5G Vision-The 5G Infrastructure Public Private Partnership: the next generation of communication networks and services. https://5g-ppp.eu/wp-content/uploads/2015/02/5G-Vision-Brochure-v1.pdf, 2017-10-24.
Li, X., Wang, X., Li, K., et al. (2017). CaaS: Caching as a service for 5G networks. IEEE Access,5, 5982–5993.
Haider, F. (2014). Cellular architecture and key technologies for 5G wireless communication networks. IEEE Communications Magazine,52(2), 122–130.
Zhou, C., Jiang, H., Chen, Y., et al. (2016). User interest acquisition by adding home and work related contexts on mobile big data analysis. In 2016 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE.
Zhou, C., Jiang, H., Chen, Y., Wu, J., & Wu, Y. (2016). TCB: A feature transformation method based central behavior for user interest prediction on mobile big data. International Journal of Distributed Sensor Networks. https://doi.org/10.1177/1550147716671256
Agiwal, M., Roy, A., & Saxena, N. (2017). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials,18(3), 1617–1655.
Bastug, E., Bennis, M., & Debbah, M. (2014). Living on the edge: The role of proactive caching in 5G wireless networks. IEEE Communications Magazine,52(8), 82–89.
Ramanan, B. A., Drabeck, L. M., Haner, M., Nithi, N., Klein, T. E., & Sawkar, C. (2013). Cacheability analysis of HTTP traffic in an operational LTE network. In Wireless telecommunications symposium (WTS). IEEE, Phoenix, AZ, USA.
Chen, Z., Lee, J., Quek, T. Q. S., et al. (2016). Cluster-centric cache utilization design in cooperative small cell networks. In IEEE international conference on communications. IEEE.
Chen, Z., Lee, J., Quek, T. Q. S., et al. (2017). Cooperative caching and transmission design in cluster-centric small cell networks. IEEE Transactions on Wireless Communications,16(5), 3401–3415.
Fan, S., Zheng, J., & Xiao, J. (2015). A clustering-based downlink resource allocation algorithm for small cell networks. In 2015 international conference on wireless communications & signal processing (WCSP). IEEE.
Yan, H., Gao, D., Su, W., et al. (2017). Caching strategy based on hierarchical cluster for named data networking. IEEE Access,5, 8433–8443.
Elbamby, M. S., Bennis, M., Saad, W., et al. (2014). Content-aware user clustering and caching in wireless small cell networks. In International Symposium on Wireless Communications Systems.
Hajri, S. E., & Assaad, M. (2016). Caching improvement using adaptive user clustering. In IEEE international workshop on signal processing advances in wireless communications. IEEE.
Poularakis, K., Iosifidis, G., & Tassiulas, L. (2014). Approximation caching and routing algorithms for massive mobile data delivery. In Global communications conference. IEEE.
Yu, R., et al. (2016). Enhancing software-defined RAN with collaborative caching and scalable video coding. In 2016 IEEE international conference on communications (ICC). IEEE.
Jiang, W., Feng, G., & Qin, S. (2017). Optimal cooperative content caching and delivery policy for heterogeneous cellular networks. IEEE Transactions on Mobile Computing,16(5), 1382–1393.
Borst, S. C., Gupta, V., & Walid, A. (2010). Distributed caching algorithms for content distribution networks. In Conference on information communications. IEEE Press.
Ranaweera, C., Wong, E., Lim, C., et al. (2012). Next generation optical-wireless converged network architectures. IEEE Network,26(2), 22–27.
Wang, S., Zhang, X., Yang, K., et al. (2016). Distributed edge caching scheme considering the tradeoff between the diversity and redundancy of cached content. In IEEE/CIC international conference on communications in China. IEEE.
Poularakis, K., Iosifidis, G., & Tassiulas, L. (2014). Approximation caching and routing algorithms for massive mobile data delivery. In Global communications conference. IEEE.
Sermpezis, P., Spyropoulos, T., Vigneri, L., et al. (2017). Femto-caching with soft cache hits: Improving performance through recommendation and delivery of related content. IEEE Journal on Selected Areas in Communications, 36(6),1300–1313
Shanmugam, K., Golrezaei, N., Dimakis, A. G., Molisch, A. T., & Caire, G. (2012). FemtoCaching: Wireless video content delivery through distributed caching helpers. IEEE Transactions on Information Theory, 59(12), 8402–8413
Borst, S. C., Gupta, V., & Walid, A. (2010). Distributed caching algorithms for content distribution networks. In Conference on Information Communications. IEEE Press.
Jiang, M., Cui, P., Wang, F., et al. (2014). FEMA: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery. In ACM.
Acar, E., Çamtepe, S. A., Krishnamoorthy, M. S., et al. (2005). Modeling and multiway analysis of chatroom tensors. In International conference on intelligence and security informatics. Springer, Berlin.
Sidiropoulos, N. D. (2016) Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM.
Schein, A., Zhou, M., Blei, D. M., Wallach, H. (2016). Bayesian Poisson Tucker decomposition for learning the structure of international relations. In: ICML'16 Proceedings of the 33rd international conference on international conference on machine learning (vol. 48, pp. 2810–2819). New York, NY, USA
Joachims, T. (2006). Training linear SVMs in linear time. In ACM Sigkdd international conference on knowledge discovery & data mining. ACM.
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Yang, L., Chen, Y., Li, L. et al. Cooperative caching and delivery algorithm based on content access patterns at network edge. Wireless Netw 26, 1587–1600 (2020). https://doi.org/10.1007/s11276-019-02148-7
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DOI: https://doi.org/10.1007/s11276-019-02148-7