Abstract
In this paper, we compare and contrast model-based and machine learning approaches for designing caching and routing strategies to improve cache network performance (e.g., delay, hit rate). We first outline the key principles used in the design of model-based strategies and discuss the analytical results and bounds obtained for these approaches. By conducting experiments on real-world traces and networks, we identify the interplay between content popularity skewness and request stream correlation as an important factor affecting cache performance. With respect to routing, we show that the main factors impacting performance are alternate path routing and content search. We then discuss the applicability of multiple machine learning models, specifically reinforcement learning, deep learning, transfer learning and probabilistic graphical models for the caching and routing problem.
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References
Banerjee, A., Banerjee, B., Seetharam, A., Tellambura, C.: Content search and routing under custodian unavailability in information-centric networks. Comput. Netw. 141, 92–101 (2018)
Banerjee, B., Kulkarni, A., Seetharam, A.: Greedy caching: an optimized content placement strategy for information-centric networks. Comput. Netw. 140, 78–91 (2018)
Banerjee, B., Seetharam, A., Mukherjee, A., Naskar, M.K.: Characteristic time routing in information-centric networks. Comput. Netw. 113, 148–158 (2017)
Banerjee, B., Seetharam, A., Tellambura, C.: Greedy caching: a latency-aware caching strategy for information-centric networks. In: 2017 International Conference on Networking. IFIP (2017)
Dehghan, M., et al.: On the complexity of optimal request routing and content caching in heterogeneous cache networks. IEEE/ACM Trans. Network. (TON) 25(3), 1635–1648 (2017)
Din, I.U., Hassan, S., Khan, M.K., Guizani, M., Ghazali, O., Habbal, A.: Caching in information-centric networking: strategies, challenges, and future research directions. IEEE Commun. Surv. Tutor. 20(2), 1443–1474 (2018)
Garetto, M., Leonardi, E., Martina, V.: A unified approach to the performance analysis of caching systems. ACM Trans. Model. Perform. Eval. Comput. Syst. 1(3), 12 (2016)
Herath, J.D., Seetharam, A.: Analyzing opportunistic request routing in wireless cache networks. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2018)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
Kulkarni, A., Seetharam, A.: Exploiting correlations in request streams: a case for hybrid caching in cache networks. In: 2018 IEEE 43rd Conference on Local Computer Networks (LCN), pp. 562–570. IEEE (2018)
Milad Mahdian, A.M., Ioannidis, S., Yeh, E.: Kelly cache networks. In: 2019 IEEE International Conference on Computer Communications (INFOCOM). IEEE (2019)
Narayanan, A., Verma, S., Ramadan, E., Babaie, P., Zhang, Z.L.: DeepCache: a deep learning based framework for content caching. In: Proceedings of the 2018 Workshop on Network Meets AI & ML, pp. 48–53. ACM (2018)
Paschos, G.S., Iosifidis, G., Tao, M., Towsley, D., Caire, G.: The role of caching in future communication systems and networks. IEEE J. Sel. Areas Commun. 36(6), 1111–1125 (2018)
Qiu, L., Cao, G.: Popularity-aware caching increases the capacity of wireless networks. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp. 1–9 (2017)
Qiu, L., Cao, G.: Cache increases the capacity of wireless networks. In: IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Rizk, A., Zink, M., Sitaraman, R.: Model-based design and analysis of cache hierarchies. In: 2017 International Conference on Networking. IFIP (2017)
Rosensweig, E.: On the analysis and management of cache networks (2012)
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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Kulkarni, A., Seetharam, A. (2021). Model-Based and Machine Learning Approaches for Designing Caching and Routing Algorithms. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_2
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DOI: https://doi.org/10.1007/978-3-030-67369-7_2
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