An Adaptive Edge-Computing Framework for Low-Latency Multimedia Content Delivery Networks

International Journal of Computer Science and Technology | Vol. 7, No. 8, August 2005 | pp. 57–64

DOI: 10.46882/2005/IJCST/070811

Research Article

Title: An Adaptive Edge-Computing Framework for Low-Latency Multimedia Content Delivery Networks

Names of Authors: Kofi Mensah¹, Hiroshi Tanaka²

Authors’ Affiliations: ¹Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; ²Department of Information and Communication Engineering, University of Tokyo, Tokyo, Japan

Abstract:
The increasing consumer demand for high-definition streaming media presents substantial bandwidth bottlenecks for legacy centralized content delivery networks (CDNs). This study introduces an adaptive edge-computing framework designed to decentralize caching operations by deploying localized data staging nodes at the network periphery. The distribution engine evaluates real-time client request matrices and uses a proactive prediction model to push popular media blocks during off-peak hours. Simulations were executed using synthetic traffic profiles across a modeled regional network topology with 150 edge cache layers. The experimental data show that the edge framework reduces the core backbone network utilization by 38.4%, while improving the final end-user content delivery latency from t = 142 ms down to an average of t = 34 ms ± 4 ms under highly congested request scenarios. Additionally, the cache hit ratio (CHR) stabilized at a highly efficient index of 92.15%, validating the system's capacity to minimize backhaul congestion and scale smoothly under sudden, high-volume user traffic spikes.

Keywords: Edge Computing, Content Delivery Networks, Cache Optimization, Low Latency, Multimedia Streaming

Manuscript Timeline: Received May 10, 2005; Revised June 22, 2005; Accepted July 14, 2005; Published August 26, 2005

Citation: Mensah, K., & Tanaka, H. (2005). An Adaptive Edge-Computing Framework for Low-Latency Multimedia Content Delivery Networks. International Journal of Computer Science and Technology, 7(8), 57–64.

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