Open Access
Research Article
A mathematical congestion-aware Dijkstra optimization model for adaptive routing in CCDN
Rohit Kumar Guptar.k.7878@gmail.comDepartment of Computer Science & Engineering Malaviya National Institute of Technology Jaipur; Department of Information Technology Manipal University JaipurDepartment of Information Technology Manipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , *Arka Prokash MazumdarCorresponding authorapmazumdar.cse@mnit.ac.inDepartment of Computer Science & Engineering Malaviya 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:
- 01 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2243
- Pages:
- 1591–1601
Abstract
Today’s content delivery over the Internet via Cloud-based Content Delivery Networks (CCDNs) must address critical challenges such as dynamic traffic, congestion, inefficient routing, and latency variations, which are not effectively handled by the traditional Dijkstra’s algorithm due to its static cost assignments. To overcome these challenges, a congestion-aware collaborative Dijkstra optimization approach guided by the Proximal Policy Optimization (PPO) method is proposed. This PPO-Guided Dijkstra integrates graph-theoretic path computation with optimization-driven cost adjustments by considering real-time congestion data. Simulation-based assessments across various network conditions indicate that the proposed model achieves a 33% reduction in latency, a 6.7% improvement in resilience under node failure scenarios, and significantly faster convergence in comparison to conventional and non-collaborative routing strategies. These findings demonstrate the scalable, congestion-aware routing solution for next-generation CCDNs.
Keywords
Subject Classifications
Primary 68M10Secondary 68T0594C15
References
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