This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource predictio...
详细信息
ISBN:
(纸本)9781467364324
This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resourceprediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.
This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource predictio...
详细信息
ISBN:
(纸本)9781467364300
This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resourceprediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.
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