PROJECT TITLE :

Resource Allocation With Video Traffic Prediction in Cloud-Based Space Systems

ABSTRACT:

This paper considers the resource allocation problems for video transmission in space-primarily based data networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to create configurations over time to attenuate the time average cost of the system, and to attenuate the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be happy. During this paper, we have a tendency to introduce a predictive backpressure algorithm, that considers the long run arrivals with a bound prediction window size into the thought of resource allocation to make decisions on that packets to be served initial. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system will be reduced through this prediction-primarily based resource allocation, and the prediction accuracy for the video traffic is improved consistent with the proposed prediction system.