A mathematical congestion-aware Dijkstra optimization model for adaptive routing in CCDN
Rohit Kumar Gupta2018rcp9091@mnit.ac.inrohitkumar.gupta@jaipur.manipal.eduAffiliation 1Department of Computer Science & EngineeringMalaviya National Institute of Technology JaipurJaipur, Rajasthan, 302017, IndiaAffiliation 2Department of Information TechnologyManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , *Arka Prokash MazumdarCorresponding authorapmazumdar.cse@mnit.ac.inDepartment of Computer Science & EngineeringMalaviya National Institute of Technology JaipurJaipur, Rajasthan, 302017, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 04 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2243
- Pages:
- 1591–1601
Abstract
Keywords
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References
[1] C. Papagianni, A. Leivadeas, and S. Papavassiliou, “A cloud-oriented content delivery network paradigm: Modeling and assessment,” IEEE Trans. Dependable Secure Comput., vol. 10, no. 5, pp. 287–300 (2013).
[2] Y.-W. Ma, J.-L. Chen, C.-C. Chang, A. Nakao, and S. Yamamoto, “A novel dynamic resource adjustment architecture for virtual tenant networks in SDN,” J. Syst. Softw., vol. 143, pp. 100–115 (2018).
[3] M. Mangili, J. Elias, F. Martignon, and A. Capone, “Optimal planning of virtual content delivery networks under uncertain traffic demands,” Comput. Netw., vol. 106, pp. 186–195 (2016).
[4] Y. Liu, D. Lu, G. Zhang, J. Tian, and W. Xu, “Q-learning based content placement method for dynamic cloud content delivery networks,” IEEE Access, vol. 7, pp. 66384–66394 (2019).
[5] M. He, D. Lu, J. Tian, and G. Zhang, “Collaborative reinforcement learning based route planning for cloud content delivery networks,” IEEE Access, vol. 9, pp. 30868–30880 (2021).
[6] M. Pooyandeh and I. Sohn, “Edge network optimization based on AI techniques: A survey,” Electronics, vol. 10, no. 22, pp. 2830 (2021).
[7] W. Funika, P. Koperek, and J. Kitowski, “Automated cloud resources provisioning with the use of the proximal policy optimization,” J. Supercomput., vol. 79, no. 6, pp. 6674–6704 (2023).
[8] T. T. Nguyen, N. D. Nguyen, and S. Nahavandi, “Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3826–3839 (Sep. 2020).
[9] A. Sadeghi, G. Wang, and G. B. Giannakis, “Deep reinforcement learning for adaptive caching in hierarchical content delivery networks,” IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 4, pp. 1024–1033 (Dec. 2019).
[10] Q. Cai, C. Cui, Y. Xiong, W. Wang, Z. Xie, and M. Zhang, “A survey on deep reinforcement learning for data processing and analytics,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 5, pp. 4446–4465 (May 2023).
[11] M. Sasikumar, P. J. Jayarin, and F. S. F. Vinnarasi, “A hierarchical optimized resource utilization based content placement (HORCP) model for cloud content delivery networks (CDNs),” J. Cloud Comput., vol. 12, no. 1, pp. 139 (2023).
[12] H. Taheri, S. R. Hosseini, and M. A. Nekoui, “Deep reinforcement learning with enhanced PPO for safe mobile robot navigation,” arXiv preprint, arXiv:2405.16266 (2024).
[13] Li Keqin, Lipeng Liu, Jiajing Chen, Dezhi Yu, Xiaofan Zhou, Ming Li, Congyu Wang, and Zhao Li, “Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout,” in Proc. 2024 6th Int. Conf. Artif. Intell. Comput. Appl. (ICAICA), pp. 296–301 (2024).
[14] R. K. Gupta and A. P. Mazumdar, “Cost-aware and time-varying request rate-based dynamic cache provisioning over CCDN,” in Proc. IEEE Int. Conf. Adv. Netw. Telecommun. Syst. (ANTS), Jaipur, India, pp. 610–615 (2023).
[15] Y. Xiao, H. Yu, Y. Yang, Y. Wang, J. Liu, and N. Ansari, “Adaptive joint routing and caching in knowledge-defined networking: An actor-critic deep reinforcement learning approach,” IEEE Trans. Mobile Comput., vol. 24, no. 5, pp. 4118–4135 (May 2025).
[16] H. Cheng, L. Yang, Q. Zhang, and W. Zhu, “Reliable routing and scheduling in time-sensitive networks based on reinforcement learning,” IEEE Trans. Netw. Sci. Eng. (2025).
[17] R. K. Gupta and A. P. Mazumdar, “Cost-balanced adaptive replica server cache management under time-varying user request density in CCDNs,” in Proc. IEEE Int. Conf. Commun. Syst. Netw. Technol. (CSNT), Bhopal, India, pp. 696–702 (2025).
[18] V. R. Bolla, B. Vikas, S. Potluri, Y. Subbarayudu, G. Sucharitha, and N. Narisetty, “Scalable cloud-based reinforcement learning for multimedia data in cognitive neuroscience for secure healthcare analysis,” J. Inf. Optim. Sci., vol. 46, no. 6, pp. 1793–1801 (2025).
[19] N. Tuli, R. Phursule, S. Bansal, B. M. Nanche, S. Katoch, and A. Raina, “Investigating discrete structures in network algorithms for optimizing traffic flow and reducing congestion in urban areas,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 2-B, pp. 849–856 (2026).
[20] O. Aruna and A. Sharma, “An adaptive routing protocol in flying ad hoc networks,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 3, pp. 757–770 (2022).
