Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To guarantee quality of service (QoS) in future integrated service networks, traffic sources must be characterized to capture the traffic characteristics relevant to network performance. Recent studies reveal that multimedia traffic shows burstiness over multiple time scales and long range dependence (LRD). While researchers agree on the importance of traffic correlation, there is no agreement on how much correlation should be incorporated into a traffic model for performance estimation and dimensioning of networks. In this article,  present an approach for defining a relevant time scale for the characterization of VBR video traffic in the sense of queueing delay.  first consider the Reich formula and characterize traffic by the Piecewise Linear Arrival Envelope Function (PLAEF).  then define the cutoff interval above which the correlation does not affect the queue buildup. The cutoff interval is the upper bound of the time scale which is required for the estimation of queue size and thus the characterization of VBR video traffic.  also give a procedure to approximate the empirical PLAEF with a concave function; this significantly simplifies the calculation in the estimation of the cutoff interval and delay bound with little estimation loss.  quantify the relationship beten the time scale in the correlation of video traffic and the queue buildup using a set of experiments with traces of MPEG/JPEG-compressed video.  show that the critical interval, i.e. the range for the correlation relevant to the queueing delay, depends on the traffic load: as the traffic load increases, the range of the time scale required for estimation for queueing delay also increases. These results offer further insights into the implication of LRD in VBR video traffic.
['Heejune Ahn', 'Andrea Baiocchi', 'Jae-Kyoon Kim']
I am writing a research paper on a idea On the time scales in video traffic characterization for queueing behavior
[{"from": "human", "value": "I am writing a research paper on a idea On the time scales in video traffic characterization for queueing behavior"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To guarantee quality of service (QoS) in future integrated service networks, traffic sources must be characterized to capture the traffic characteristics relevant to network performance. Recent studies reveal that multimedia traffic shows burstiness over multiple time scales and long range dependence (LRD). While researchers agree on the importance of traffic correlation, there is no agreement on how much correlation should be incorporated into a traffic model for performance estimation and dimensioning of networks. In this article,  present an approach for defining a relevant time scale for the characterization of VBR video traffic in the sense of queueing delay.  first consider the Reich formula and characterize traffic by the Piecewise Linear Arrival Envelope Function (PLAEF).  then define the cutoff interval above which the correlation does not affect the queue buildup. The cutoff interval is the upper bound of the time scale which is required for the estimation of queue size and thus the characterization of VBR video traffic.  also give a procedure to approximate the empirical PLAEF with a concave function; this significantly simplifies the calculation in the estimation of the cutoff interval and delay bound with little estimation loss.  quantify the relationship beten the time scale in the correlation of video traffic and the queue buildup using a set of experiments with traces of MPEG/JPEG-compressed video.  show that the critical interval, i.e. the range for the correlation relevant to the queueing delay, depends on the traffic load: as the traffic load increases, the range of the time scale required for estimation for queueing delay also increases. These results offer further insights into the implication of LRD in VBR video traffic."}]