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Abstract
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Article Information:
An Improved Particle Swarm Optimization Based on Deluge Approach for Enhanced Hierarchical Cache Optimization in IPTV Networks
M. Somu and N. Rengarajan
Corresponding Author: M. Somu
Submitted: November 07, 2013
Accepted: November 18, 2013
Published: May 15, 2014 |
Abstract:
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In recent years, IP network has been considered as a new delivery network for TV services. A majority of the telecommunication industries have used IP network to offer on-demand services and linear TV services as it can offer a two-way and high-speed communication. In order to effectively and economically utilize the IP network, caching is the technique which is usually preferred. In IPTV system, a managed network is utilized to bring out TV services, the requests of Video on Demand (VOD) objects are usually combined in a limited period intensively and user preferences are fluctuated dynamically. Furthermore, the VOD content updates often under the control of IPTV providers. In order to minimize this traffic and overall network cost, a segment of the video content is stored in caches closer to subscribers, for example, Digital Subscriber Line Access Multiplexer (DSLAM), a Central Office (CO) and Intermediate Office (IO). The major problem focused in this approach is to determine the optimal cache memory that should be assigned in order to attain maximum cost effectiveness. This approach uses an effective Grate Deluge algorithm based Particle Swarm Optimization (GDPSO) approach for attaining the optimal cache memory size which in turn minimizes the overall network cost. The analysis shows that hierarchical distributed caching can save significant network cost through the utilization of the GDPSO algorithm.
Key words: Digital Subscriber Line Access Multiplexer (DSLAM), Grate Deluge (GD) algorithm, IPTV, PSO, Video on Demand (VOD), ,
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Cite this Reference:
M. Somu and N. Rengarajan, . An Improved Particle Swarm Optimization Based on Deluge Approach for Enhanced Hierarchical Cache Optimization in IPTV Networks. Research Journal of Applied Sciences, Engineering and Technology, (19): 4018-4028.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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