Learning From Images: Proactive Caching With Parallel Convolutional Neural Networks
Authors: 
Yantong Wang, Ye Hu, Zhaohui Yang, Walid Saad, Kai-Kit Wong, Vasilis FriderikosAuthors Info & Claims
Pages 7234 - 7248
Abstract
With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various Mixed Integer Linear Programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, Convolutional Neural Networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the network resources (such as caching space and link bandwidth) competition among sub-problems, the CNNs’ outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs’ outputs to accelerate local search. Numerical results show that the proposed scheme can reduce <inline-formula><tex-math notation="LaTeX">$71.6\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>71</mml:mn><mml:mo>.</mml:mo><mml:mn>6</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3207209.gif"/></alternatives></inline-formula> computation time, whose computation time reaches around 28.9ms, with only <inline-formula><tex-math notation="LaTeX">$0.8\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>8</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq2-3207209.gif"/></alternatives></inline-formula> additional performance cost compared to the MILP solution, which provides high quality decision making in pseudo real-time.
References
[1]
S. Wang, X. Zhang, Y. Zhang, L. Wang, J. Yang, and W. Wang, “A survey on mobile edge networks: Convergence of computing, caching and communications,” IEEE Access, vol. 5, pp. 6757–6779, 2017.
[2]
X. Vasilakos, V. A. Siris, G. C. Polyzos, and M. Pomonis, “Proactive selective neighbor caching for enhancing mobility support in information-centric networks,” in Proc. 2nd Ed. ICN Workshop Inf.-Centric Netw., 2012, pp. 61–66.
[3]
X. Vasilakos, V. A. Siris, and G. C. Polyzos, “Addressing niche demand based on joint mobility prediction and content popularity caching,” Comput. Netw., vol. 110, pp. 306–323, 2016.
[4]
V. Sourlas, G. S. Paschos, P. Flegkas, and L. Tassiulas, “Mobility support through caching in content-based publish/subscribe networks,” in Proc. 10th IEEE/ACM Int. Conf. Cluster Cloud Grid Comput., 2010, pp. 715–720.
[5]
U. Farooq, E. W. Parsons, and S. Majumdar, “Performance of publish/subscribe middleware in mobile wireless networks,” Proc. ACM SIGSOFT Softw. Eng. Notes, vol. 29, pp. 278–289, 2004.
[6]
A. Gaddah and T. Kunz, “Extending mobility to publish/subscribe systems using a pro-active caching approach,” Mobile Informat. Syst., vol. 6, no. 4, pp. 293–324, Dec. 2010.
[7]
E. Bastug, M. Bennis, and M. Debbah, “Living on the edge: The role of proactive caching in 5G wireless networks,” IEEE Commun. Mag., vol. 52, no. 8, pp. 82–89, Aug. 2014.
[8]
Y. Wang and V. Friderikos, “A survey of deep learning for data caching in edge network,” Informatics, vol. 7, no. 4, pp. 43–71, 2020.
[9]
Z. Yang et al., “Cache placement in two-tier hetnets with limited storage capacity: Cache or buffer?,” IEEE Trans. Commun., vol. 66, no. 11, pp. 5415–5429, Jun. 2018.
[10]
J. Yang, C. Ma, B. Jiang, G. Ding, G. Zheng, and H. Wang, “Joint optimization in cached-enabled heterogeneous network for efficient industrial iot,” IEEE J. Sel. Areas Commun., vol. 38, no. 5, pp. 831–844, Mar. 2020.
[11]
Y. Wang, G. Zheng, and V. Friderikos, “Proactive caching in mobile networks with delay guarantees,” in Proc. IEEE Int. Conf. Commun., 2019, pp. 1–6.
[12]
G. Zheng and V. Friderikos, “Optimal proactive cache management in mobile networks,” in Proc. IEEE Int. Conf. Commun., 2016, pp. 1–6.
[13]
C. Fang, F. R. Yu, T. Huang, J. Liu, and Y. Liu, “An energy-efficient distributed in-network caching scheme for green content-centric networks,” Comput. Netw., vol. 78, pp. 119–129, 2015.
[14]
J. Zou, C. Li, C. Zhai, H. Xiong, and E. Steinbach, “Joint pricing and cache placement for video caching: A game theoretic approach,” IEEE J. Sel. Areas Commun., vol. 37, no. 7, pp. 1566–1583, Jul. 2019.
[15]
N. Golrezaei, K. Shanmugam, A. G. Dimakis, A. F. Molisch, and G. Caire, “FemtoCaching: Wireless video content delivery through distributed caching helpers,” in Proc. IEEE Int. Conf. Comput. Commun., 2012, pp. 1107–1115.
[16]
J. Tadrous, A. Eryilmaz, and H. E. Gamal, “Proactive content download and user demand shaping for data networks,” IEEE/ACM Trans. Netw., vol. 23, no. 6, pp. 1917–1930, Dec. 2015.
[17]
Q. Chen, F. R. Yu, T. Huang, R. Xie, J. Liu, and Y. Liu, “Joint resource allocation for software-defined networking, caching, and computing,” IEEE/ACM Trans. Netw., vol. 26, no. 1, pp. 274–287, Feb. 2018.
[18]
S. Shan, C. Feng, T. Zhang, and J. K.-K. Loo, “Proactive caching placement for arbitrary topology with multi-hop forwarding in ICN,” IEEE Access, vol. 7, pp. 149117–149131, 2019.
[19]
Y. Li, H. Ma, L. Wang, S. Mao, and G. Wang, “Optimized content caching and user association for edge computing in densely deployed heterogeneous networks,” IEEE Trans. Mobile Comput., vol. 21, no. 6, pp. 2130–2142, Jun. 2022.
[20]
T. Li, T. Braud, Y. Li, and P. Hui, “Lifecycle-aware online video caching,” IEEE Trans. Mobile Comput., vol. 20, no. 8, pp. 2624–2636, 2021.
[21]
L. Lei, Y. Yuan, T. X. Vu, S. Chatzinotas, and B. Ottersten, “Learning-based resource allocation: Efficient content delivery enabled by convolutional neural network,” in Proc. IEEE 20th Int. Workshop Signal Process. Adv. Wirel. Commun., 2019, pp. 1–5.
[22]
L. Lei, L. You, G. Dai, T. X. Vu, D. Yuan, and S. Chatzinotas, “A deep learning approach for optimizing content delivering in cache-enabled hetnet,” in Proc. IEEE Int. Symp. Wirel. Commun. Syst., 2017, pp. 449–453.
[23]
M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, “Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,” IEEE J. Sel. Areas Commun., vol. 35, no. 5, pp. 1046–1061, Mar. 2017.
[24]
K. C. Tsai, L. L. Wang, and Z. Han, “Caching for mobile social networks with deep learning: Twitter analysis for 2016 U.S. election,” IEEE Trans. Netw. Sci. Eng., vol. 7, no. 1, pp. 193–204, Jan.–Mar. 2020.
[25]
L. Ale, N. Zhang, H. Wu, D. Chen, and T. Han, “Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network,” IEEE Internet Things J., vol. 6, no. 3, pp. 5520–5530, Mar. 2019.
[26]
Q. Fan, X. Li, J. Li, Q. He, K. Wang, and J. Wen, “PA-cache: Evolving learning-based popularity-aware content caching in edge networks,” IEEE Trans. Netw. Service Manag., vol. 18, no. 2, pp. 1746–1757, Jun. 2021.
[27]
Y. Qian, R. Wang, J. Wu, B. Tan, and H. Ren, “Reinforcement learning-based optimal computing and caching in mobile edge network,” IEEE J. Sel. Areas Commun., vol. 38, no. 10, pp. 2343–2355, Oct. 2020.
[28]
Y. He, N. Zhao, and H. Yin, “Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach,” IEEE Trans. Veh. Technol, vol. 67, no. 1, pp. 44–55, Jan. 2018.
[29]
L. Li et al., “Deep reinforcement learning approaches for content caching in cache-enabled D2D networks,” IEEE Internet Things J., vol. 7, no. 1, pp. 544–557, Jan. 2020.
[30]
C. Zhong, M. C. Gursoy, and S. Velipasalar, “Deep reinforcement learning-based edge caching in wireless networks,” IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 1, pp. 48–61, Mar. 2020.
[31]
Y. Wang and V. Friderikos, “Caching as an image characterization problem using deep convolutional neural networks,” in Proc. IEEE Int. Conf. Commun., 2020, pp. 1–6.
[32]
V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proc. IEEE, vol. 105, no. 12, pp. 2295–2329, Nov. 2017.
[33]
A. Nowak, S. Villar, A. S. Bandeira, and J. Bruna, “Revised note on learning quadratic assignment with graph neural networks,” in Proc. IEEE Data Sci. Workshop, 2018, pp. 1–5.
[34]
G. Xylomenos et al., “A survey of information-centric networking research,” IEEE Commun. Surv. Tut., vol. 16, no. 2, pp. 1024–1049, Jul. 2013.
[35]
P. Van Mieghem, Performance Analysis of Communications Networks and Systems. Cambridge, U.K.: Cambridge Univ. Press, 2014.
[36]
G. S. Malkin, “RIP version 2,” IETF Network Working Group RFC 2453, Nov. 1998.
[37]
J. Moy, “OSPF version 2,” IETF Network Working Group RFC 2328, Apr. 1998.
[38]
O. Karrakchou, N. Samaan, and A. Karmouch, “A survey of routing mechanisms in ICNs,” in Proc. IEEE 6th Int. Conf. Multimedia Comput. Syst., 2018, pp. 1–6.
[39]
B. Nour et al., “A survey of internet of things communication using ICN: A use case perspective,” Comput. Commun., vol. 142/143, pp. 95–123, 2019.
[40]
M. Zhang, H. Luo, and H. Zhang, “A survey of caching mechanisms in information-centric networking,” IEEE Commun. Surv. Tut., vol. 17, no. 3, pp. 1473–1499, Apr. 2015.
[41]
M.-L. Zhang and Z.-H. Zhou, “A review on multi-label learning algorithms,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 8, pp. 1819–1837, Mar. 2013.
[42]
A. Al-Molegi, M. Jabreel, and A. Martínez-Ballesté, “Move, attend and predict: An attention-based neural model for people's movement prediction,” Pattern Recognit. Lett., vol. 112, no. 5, pp. 34–40, 2018.
[43]
MEC ETSI Industry Specification Group, “Multi-access edge computing (MEC); framework and reference architecture,” White Paper GS-MEC-003-V2.2.1, ETSI, 2020. [Online]. Available: https://www.etsi.org/deliver/etsi_gs/MEC/001_099/003/02.02.01_60/gs_MEC003v020201p.pdf
[44]
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang, “On large-batch training for deep learning: Generalization gap and sharp minima,” in Proc. Int. Conf. Learn. Representation, 2017, pp. 1–16.
[45]
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 269–283, Jan. 2021.
[46]
Y. Hu, M. Chen, W. Saad, H. V. Poor, and S. Cui, “Distributed multi-agent meta learning for trajectory design in wireless drone networks,” IEEE J. Sel. Areas Commun., vol. 39, no. 10, pp. 3177–3192, Oct. 2021.
Index Terms
- Learning From Images: Proactive Caching With Parallel Convolutional Neural Networks
Index terms have been assigned to the content through auto-classification.
Recommendations
Selective Victim Caching: A Method to Improve the Performance of Direct-Mapped Caches
Although direct-mapped caches suffer from higher miss ratios as compared to set-associative caches, they are attractive for today's high-speed pipelined processors that require very low access times. Victim caching was proposed by Jouppi [1] as an ...
Cooperative caching for adaptive bit rate streaming in content delivery networks
IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and CommunicationThis work proposes a cooperative caching model which supports adaptive bit-rate streaming in content delivery networks. A linear program (LP) problem is applied to maximize the total user satisfaction. The optimal content placement and content fetching ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
1536-1233 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Educational Activities Department
United States
Publication History
Published: 16 September 2022
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in