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On the energy-delay trade-off in CCN caching strategy: a multi-objective optimization problem

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Abstract

In recent years, content-centric networks (CCN) have introduced the significant feature of in-network caching, which saves transmission energy consumption in content distribution. However, because of the extra logic needed for the caching mechanism, one of these networks’ main challenges is optimizing the trade-off between transmission and caching energy consumption. Moreover, in an energy-aware CCN, less popular content is cached near the content provider despite more popular content caching near end users. Therefore, in real-time or delay-sensitive traffic with less popularity, this caching strategy degrades the quality of service, drops delayed chunks, and wastes energy consumption. Accordingly, designing an appropriate content caching policy to improve energy efficiency and service quality is a long-term goal of the green CCN. This paper considers minimizing energy consumption and the queuing delay in CCN as a multi-objective optimization problem. Thus, to drive the proposed approach, called ED-CCN-MOP, the CCN queuing delay for receiving the Interest and Data packets is analyzed and formulated. Furthermore, the ED-CCN-MOP model is solved using the proposed Non-dominated Sorting Markov Approximation (NSMA) method. According to the numerical results, the NSMA algorithm outperforms the NSGA-II, NSGA-III, and MODA algorithms by about 49%, 46%, and 38%, respectively, in terms of their average energy-delay-product metric with the possibility of distributed implementation. Furthermore, the quality of NSMA solutions is evaluated and compared using performance metrics. The results of this evaluation indicate that NSMA consistently achieves a high level of performance.

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References

  1. Jacobson, V., Smetters, D. K., Thornton, J. D., Plass, M. F., Briggs, N. H., & Braynard, R. L. (2009). Networking named content. In Proceedings of the 5th international conference on Emerging networking experiments and technologies (pp. 1–12). ACM.

  2. Furqan, M., Yan, W., Zhang, C., Iqbal, S., Jan, Q., & Huang, Y. (2019). An energy-efficient collaborative caching scheme for 5g wireless network. IEEE Access, 7, 156907–156916. https://doi.org/10.1109/ACCESS.2019.2949272

    Article  Google Scholar 

  3. Lee, U., Rimac, I., Kilper, D., & Hilt, V. (2011). Toward energy-efficient content dissemination. IEEE Network, 25(2), 14–19. https://doi.org/10.1109/MNET.2011.5730523

    Article  Google Scholar 

  4. Qazi, F., Khalid, O., Rais, R. N. B., Khan, I. A., et al. (2019). Optimal content caching in content-centric networks. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2019/6373960

    Article  Google Scholar 

  5. Siddiqa, A., Qureshi, F. F., Shah, M. A., Iqbal, R., Wahid, A., & Chang, V. (2019). CCN: A novel energy efficient greedy routing protocol for green computing. Concurrency and Computation: Practice and Experience, 31(23), e4461. https://doi.org/10.1002/cpe.4461

    Article  Google Scholar 

  6. Shan, S., Feng, C., Zhang, T., & Loo, J. (2019). Proactive caching placement for arbitrary topology with multi-hop forwarding in ICN. IEEE Access, 7, 149117–149131.

    Article  Google Scholar 

  7. Wang, S., & Ning, Z. (2022). Collaborative caching strategy in content-centric networking. In Advances in computing, informatics, networking and cybersecurity (pp. 465–511). Springer.

  8. Chen, M., Liew, S. C., Shao, Z., & Kai, C. (2013). Markov approximation for combinatorial network optimization. IEEE Transactions on Information Theory, 59(10), 6301–6327. https://doi.org/10.1109/TIT.2013.2268923

    Article  MathSciNet  Google Scholar 

  9. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  10. Choi, N., Guan, K., Kilper, D. C., & Atkinson, G. (2012). In-network caching effect on optimal energy consumption in content-centric networking. In Proceedings of IEEE international conference on communications (ICC) (pp. 2889–2894). IEEE.

  11. Llorca, J., Tulino, A. M., Guan, K., Esteban, J., Varvello, M., Choi, N., & Kilper, D. C. (2013) Dynamic in-network caching for energy efficient content delivery. In Proceedings of IEEE INFOCOM (pp. 245–249). IEEE.

  12. Imai, S., Leibnitz, K., & Murata, M. (2013). Energy efficient data caching for content dissemination networks. Journal of High Speed Networks, 19(3), 215–235. https://doi.org/10.3233/JHS-130474

    Article  Google Scholar 

  13. Fang, C., Yu, F. R., Huang, T., Liu, J., & Liu, Y. (2015). An energy-efficient distributed in-network caching scheme for green content-centric networks. Computer Networks, 78, 119–129. https://doi.org/10.1016/j.comnet.2014.09.017

    Article  Google Scholar 

  14. Gür, G. (2015). Energy-aware cache management at the wireless network edge for information-centric operation. Journal of Network and Computer Applications, 57, 33–42. https://doi.org/10.1016/j.jnca.2015.06.009

    Article  Google Scholar 

  15. Li, C., Liu, W., Wang, L., Li, M., & Okamura, K. (2015). Energy-efficient quality of service aware forwarding scheme for content-centric networking. Journal of Network and Computer Applications, 58, 241–254. https://doi.org/10.1016/j.jnca.2015.08.008

    Article  Google Scholar 

  16. Dabirmoghaddam, A., Dehghan, M., Garcia-Luna-Aceves, J. (2016). Characterizing interest aggregation in content-centric networks. In 2016 IFIP networking conference (IFIP networking) and workshops (pp. 449–457). IEEE.

  17. Pham, T. M., Minoux, M., Fdida, S., & Pilarski, M. (2017). Optimization of content caching in content-centric network. UPMC SorbonneUniversities. https://hal.sorbonne-universite.fr/hal-01016470v2

  18. Yang, S., Qiu, X., Xie, H., Guan, J., Liu, Y., & Xu, C. (2018). GDSoC: Green dynamic self-optimizing content caching in ICN-based 5G network. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.3221

    Article  Google Scholar 

  19. An, Y., & Luo, X. (2018). An in-network caching scheme based on energy efficiency for content-centric networks. IEEE Access, 6, 20184–20194. https://doi.org/10.1109/ACCESS.2018.2823722

    Article  Google Scholar 

  20. Qu, D., Wang, X., Huang, M., Li, K., Das, S. K., & Wu, S. (2018). A cache-aware social-based QoS routing scheme in information centric networks. Journal of Network and Computer Applications, 121, 20–32. https://doi.org/10.1016/j.jnca.2018.07.002

    Article  Google Scholar 

  21. Akel, A., Ahmad, A. S., & Alataki, T. (2021). Improving QoS in information central networks (ICN). International Journal of Computer Science Trends and Technology (IJCST), 8, 66.

    Google Scholar 

  22. Serhane, O., Yahyaoui, K., Nour, B., & Moungla, H. (2021). Energy-aware cache placement scheme for IoT-based ICN networks. In IEEE international conference on communications (ICC).

  23. Jaber, G., Kacimi, R., Alfredo Grieco, L., & Gayraud, T. (2020). An adaptive duty-cycle mechanism for energy efficient wireless sensor networks, based on information centric networking design. Wireless Networks, 26(2), 791–805. https://doi.org/10.1007/s11276-018-1823-z

    Article  Google Scholar 

  24. Kumar, S., Tiwari, R., Kozlov, S., & Rodrigues, J. J. (2022). Minimizing delay in content-centric networks using heuristics-based in-network caching. Cluster Computing, 25(1), 417–431. https://doi.org/10.1007/s10586-021-03405-1

    Article  Google Scholar 

  25. Tsai, P. H., Zhang, J. B., & Tsai, M. H. (2022). An efficient probe-based routing for content-centric networking. Sensors, 22(1), 341. https://doi.org/10.3390/s22010341

    Article  Google Scholar 

  26. Dehghani, F., & Movahhedinia, N. (2018). CCN energy-delay aware cache management using quantized Hopfield. Journal of Network and Systems Management, 26, 1–21. https://doi.org/10.1007/s10922-018-9453-4

    Article  Google Scholar 

  27. Dehghani, F., & Movahhedinia, N. (2019). Energy-delay-aware caching strategy in green CCN using Markov approximation. International Journal of Communication Systems, 32(15), e4109. https://doi.org/10.1002/dac.4109

    Article  Google Scholar 

  28. Zeng, L., Ni, H., & Han, R. (2021). The yellow active queue management algorithm in ICN routers based on the monitoring of bandwidth competition. Electronics, 10(7), 806. https://doi.org/10.3390/electronics10070806

    Article  Google Scholar 

  29. Wang, M., Yue, M., & Wu, Z. (2018). WinCM: A window based congestion control mechanism for NDN. In 2018 1st IEEE international conference on hot information-centric networking (HotICN) (pp. 80–86). IEEE.

  30. Zeng, L., Ni, H., & Han, R. (2020). An incrementally deployable IP-compatible-information-centric networking hierarchical cache system. Applied Sciences, 10(18), 6228. https://doi.org/10.3390/app10186228

    Article  Google Scholar 

  31. Yasuda, Y., Nakamura, R., & Ohsaki, H. (2018). A study on the impact of delayed packet forwarding in content-centric networking. In 2018 IEEE 42nd annual computer software and applications conference (COMPSAC) (vol. 1, pp. 970–972). IEEE.

  32. Xu, H., Wang, H., Hu, J., & Min, G. (2021). Analytical modelling of content transfer in information centric networks. In 2021 IEEE 24th international conference on computational science and engineering (CSE) (pp. 64–71). IEEE.

  33. Shortle, J. F., Thompson, J. M., Gross, D., & Harris, C. M. (2018). Fundamentals of queueing theory (vol. 399). Wiley.

  34. Ben-Ammar, H., & Hadjadj-Aoul, Y. (2020). A GRASP-based approach for dynamic cache resources placement in future networks. Journal of Network and Systems Management, 28, 457–477.

    Article  Google Scholar 

  35. Bürger, M., Notarstefano, G., & Allgöwer, F. (2013). A polyhedral approximation framework for convex and robust distributed optimization. IEEE Transactions on Automatic Control, 59(2), 384–395. https://doi.org/10.1109/TAC.2013.2281883

    Article  MathSciNet  Google Scholar 

  36. Harkouss, F., Fardoun, F., & Biwole, P. H. (2018). Multi-objective optimization methodology for net zero energy buildings. Journal of Building Engineering, 16, 57–71. https://doi.org/10.1016/j.jobe.2017.12.003

    Article  Google Scholar 

  37. Emmerich, M. T., & Deutz, A. H. (2018). A tutorial on multiobjective optimization: Fundamentals and evolutionary methods. Natural Computing, 17(3), 585–609. https://doi.org/10.1007/s11047-018-9685-y

    Article  MathSciNet  Google Scholar 

  38. Fricker, C., Robert, P., Roberts, J., & Sbihi, N. (2012). Impact of traffic mix on caching performance in a content-centric network. In Proceedings of IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 310–315). IEEE.

  39. Fang, C., Yu, F. R., Huang, T., Liu, J., & Liu, Y. (2015). A survey of green information-centric networking: Research issues and challenges. IEEE Communications Surveys & Tutorials, 17(3), 1455–1472. https://doi.org/10.1109/COMST.2015.2394307

    Article  Google Scholar 

  40. Deb, K., & Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. https://doi.org/10.1109/TEVC.2013.2281535

    Article  Google Scholar 

  41. Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  Google Scholar 

  42. Audet, C., Bigeon, J., Cartier, D., Le Digabel, S., & Salomon, L. (2021). Performance indicators in multiobjective optimization. European Journal of Operational Research, 292(2), 397–422.

    Article  MathSciNet  Google Scholar 

  43. Wagner, I., & Yevseyeva, I. (2021). Designing strong privacy metrics suites using evolutionary optimization. ACM Transactions on Privacy and Security (TOPS), 24(2), 1–35.

    Article  Google Scholar 

  44. Cao, T. S., Nguyen, T. T. T., Nguyen, V. S., Truong, V. H., & Nguyen, H. H. (2023). Performance of six metaheuristic algorithms for multi-objective optimization of nonlinear inelastic steel trusses. Buildings, 13(4), 868.

    Article  Google Scholar 

  45. Benallal, H., Mourchid, Y., Abouelaziz, I., Alfalou, A., Tairi, H., Riffi, J., & El Hassouni, M. (2022). A new approach for removing point cloud outliers using box plot. In Pattern recognition and tracking XXXIII (vol. 12101, pp. 63–69). SPIE.

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Authors and Affiliations

  1. Faculty of Computer Engineering, University of Kashan, Qotb-e Ravandi Blvd, Kashan, Isfahan, Iran

    Fereshte Dehghani

  2. Faculty of Computer Engineering, University of Isfahan, HezarJerib Street, Isfahan, Isfahan, Iran

    Naser Movahhedinia

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  1. Fereshte Dehghani
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Dehghani, F., Movahhedinia, N. On the energy-delay trade-off in CCN caching strategy: a multi-objective optimization problem. Wireless Netw 30, 1255–1269 (2024). https://doi.org/10.1007/s11276-023-03544-w

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