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Second, we develop an admission control algorithm that allow to ensure a maximum average delay on the server.
Figures 10 and 11 show some interesting results, the graphs for CW = 64 show maximum average delay unexpectedly.
Results presented in Figure5a have been derived using a maximum packet loss constraint P l max ≤ 0.01 and a maximum average delay constraint D l max ≤ 10.
For higher queue lengths, Q ¯ > 50, the limiting constraint is the maximum average delay, and in this case, additional increases in the queue length have a negligible effect over the throughput.
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The link is assumed to support QoS-guaranteed traffic characterized by a maximum average packet delay D l max and a target link layer packet loss rate (PLR) P l max. Figure 1 System model.
Here, the problem of resource allocation is considered with streaming traffic, which requires a maximum guaranteed average delay.
A combination of streaming traffic (which requires a maximum guaranteed average delay) and elastic traffic (with flexible rate requirements) is investigated.
Here the problem of resource allocation is considered with streaming traffic, which requires a maximum guaranteed average delay, and elastic traffic with flexible rate of requirements.
We first consider homogeneous traffic for two cases; one in which we consider that all the SUs are elastic users with flexible rate requirements and one in which we assume that all the SUs are streaming users which correspond to the real-time transfer of various signals (e.g., voice, streaming audio/video) that requires a maximum guaranteed average delay.
In addition, a sensitivity analysis with seven different traffic flow levels is conducted to measure the performances (average queue length, maximum queue length, average delay, average stop time, and number of stops per vehicle) of the proposed layout.
The MOE parameters considered in the sensitivity study were average delay, maximum queue length, average queue length, average stop time, and number of stops per vehicle.
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