Abstract
Internet web caching architecture and web caching replacement policy have been used by internet service providers (ISP) and internet content providers (ICP) to save bandwidth, offload in servers and reduce time response to the user. Thereby, the internet services have improved the performance and quality. In recent years, machine learning (ML) is increasingly developing both in hardware as well as in algorithms, which could impact web caching replacement to become more intelligent. In this paper, we present a model network and apply two algorithms: the original method and an intelligent method based on the decision tree in ML of web caching replacement. The simulation results are shown in JMT software and compare the performance of the two algorithms.
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References
Lam, H.K., Truong, N.X.: Performance analysis of hybrid web caching architecture. Am. J. Networks Commun. 4(3), 37–43 (2015)
Kim, S.: Cooperative Inter-ISP traffic control scheme based on bargaining game approach. IEEE Access 9, 31782–31791 (2021)
Sufian, R.S., Liu, T., Paul, A.: Gluon distributions and their applications to Ioffe-time distributions. Phys. Rev. D 103(3), 036007 (2021)
Ma, T., Hao, Y., Shen, W., Tian, Y., Al-Rodhaan, M.: An improved web cache replacement algorithm based on weighting and cost. IEEE Access 6, 27010–27017 (2018)
Yovita, L.V., Syambas, N.R.: Caching on named data network: a survey and future research. Int. J. Electr. Comput. Eng. (IJECE) 98(6), 4456–4466 (2018)
Rais, R.N.B., Khalid, O.: Study and analysis of mobility, security, and caching issues in CCN. Int. J. Electr. Comput. Eng. (IJECE) 10(2), 1438–1453 (2020)
Negara, R.M., Syambas, N.R.: Caching and machine learning integration methods on named data network: a survey. In: 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA), pp. 1–6 (2020)
Im, Y., Prahladan, P., Kim, T.H., Hong, Y.G., Ha, S.: SNN-cache: a practical machine learning-based caching system utilizing the inter-relationships of requests. In: 2018 52nd Annual Conference on Information Sciences and Systems (CISS), pp. 1–6 (2018)
Ali, W., Shamsuddin, S.M.: Intelligent client-side web caching scheme based on least recently used algorithm and neuro-fuzzy system. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009. LNCS, vol. 5552, pp. 70–79. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01510-6_9
Romano, S., ElAarag, H.: A neural network proxy cache replacement strategy and its implementation in the Squid proxy server. Neural Comput. Appl. 20(1), 59–78 (2011)
Cobb, J., ElAarag, H.: Web proxy cache replacement scheme based on back-propagation neural network. J. Syst. Softw. 81(9), 1539–1558 (2008)
Jarukasemratana, S., Murata, T.: Web caching replacement algorithm based on web usage data. New Gener. Comput. 31(4), 311–329 (2013)
Sulaiman, S., Shamsuddin, S.M., Forkan, F., Abraham, A.: Intelligent web caching using neurocomputing and particle swarm optimization algorithm. In: 2008 Second Asia International Conference on Modelling and Simulation (AMS), pp. 642–647 (2008)
Ma, T., Qu, J., Shen, W., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M.: Weighted greedy dual size frequency based caching replacement algorithm. IEEE Access 6, 7214–7223 (2018)
Koskela, T., Heikkonen, J., Kaski, K.: Web cache optimization with nonlinear model using object features. Comput. Networks 43(6), 805–817 (2003)
Sajeev, G.P., Sebastian, M.P.: A novel content classification scheme for web caches. Evolving Syst. 2(2), 101–118 (2011)
Julian Benadit, P., Sagayaraj Francis, F., Muruganantham, U.: Improving the performance of a proxy cache using expectation maximization with Naive Bayes classifier. In: Jain, L.C., Behera, H.S., Mandal, J.K., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining - Volume 2. SIST, vol. 32, pp. 355–368. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2208-8_33
Julian, B.P., Pahuja, K., Sidhu, M.S.: Enhancements to content caching using weighted greedy caching algorithm in information centric networking. Procedia Comput. Sci. 171, 2435–2444 (2020)
Nimishan, S., Shriparen, S.: An approach to improve the performance of web proxy cache replacement using machine learning techniques. In: 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), pp. 1–6 (2018)
Mahmoud Al-Qudah, D.A., Olanrewaju, R.F., Wong Azman, A.: Enhancement web proxy cache performance using wrapper feature selection methods with NB and J48. IOP Conf. Ser. Mater. Sci. Eng. 260, 012012 (2017)
Bertoli, M., Casale, G., Serazzi, G.: JMT: performance engineering tools for system modeling. SIGMETRICS Perform. Eval. Rev. 36(4), 10–15 (2009)
Kumar, P.V., Reddy, V.R.: Web proxy cache replacement policies using decision tree (DT) machine learning technique for enhanced performance of web proxy. Int. J. Recent Innov. Trends Comput. Commun. 2(2), 302–309 (2014)
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Xuan, T.N., Thi, V.T., Khanh, L.H. (2022). A Design Model Network for Intelligent Web Cache Replacement in Web Proxy Caching. In: Anh, N.L., Koh, SJ., Nguyen, T.D.L., Lloret, J., Nguyen, T.T. (eds) Intelligent Systems and Networks. Lecture Notes in Networks and Systems, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-19-3394-3_68
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