Quality of Service (QoS) Optimization in Mobile Broadband Using Cloud-Based Content Delivery Network

Author:

Ayankoya Folasade1,Ajayi Olubukola1,Ohwo Blaise1

Affiliation:

1. Department of Computer Science, Babcock University, Ilishan-Remo, Nigeria

Abstract

Mobile broadband utilizing Long-Term Evolution (LTE) has advanced the field of data transmission; with networks capable of providing broadband speeds to mobile broadband users. There has been a sporadic increase in the utilization of Long-Term Evolution (LTE) networks, but due to the rapid growth and utilization of network links and network services, certain issues begin to rise, such as the issue of poor Quality of Service (QoS) perceived by mobile users. Data network quality of service degrades over time when network cannot keep up with the growing demand for the network resources. The research reviewed various existing content delivery network models in order to understand the overall architecture and operations. An optimized model was developed and integrated into the existing Long-Term Evolution network models. The model was evaluated using the Network Simulator (NS-3) and Quality of Service (QoS) metrics, such as, Network Throughput, Round Trip Time, Bandwidth, Packet Loss, Jitter and Connection Ratio. The results obtained from the simulations showed that the optimized model performed better and more efficiently than previous solutions. And if implemented in Mobile Broadband, this will improve the Quality of Service, network throughput and overall performance of the network. This study concluded that cloud-based content delivery network provides a solution which would help improve the Quality of Service experience by Mobile Broadband subscribers. By actively redirecting network traffic to the nearest replica server on the network edge; thus, increasing efficiency and throughput.

Publisher

Technoscience Academy

Subject

General Medicine

Reference28 articles.

1. Abdul, W. K., Khalil, M. S., Sayed, F. A., & Abdul, B. (2015). Performance Evaluation of Real Time Traffic in LTE Networks. Institute of Communiacation Technologies., 5(7), 5-18.

2. Ahmed, H. (2009). Long Term Evolution (LTE) - A Tutorial. Network System Laboratory, 1-48.

3. Alcatel-Lucent. (2009). The LTE Network Architecture: A Comprehensive Tutorial . Strategic White Paper, 1-23.

4. Al-Mukaddim, K. P., & Rajkumar, B. (2007). A Taxonomy and Survey of Content Delivery Networks. Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, Parkville, VIC 3010, Australia, 1-44.

5. Citrix, S. (2014). Combining CDN and Transparent Caching into a Dynamic Duo: White Paper. citrix.com, 1-7.

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