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As mentioned previously, different α values represent different types of application traffic [1].
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To see the effect of various traffic characteristics, the average connection lifetime is varied from 1 to 10 min. The fuzzy rules are effected differently for different types of applications based on their QoS requirements.
Therefore, a prominent broadband access technology must be able to undertake different network traffics from different types of applications and fulfill their network requirements.
We also assume that different types of applications, running on the SUs, are generating packets with diverse traffic sensitivity [24].
Different deep learning architectures, such as CNNs and RNNs, support different types of applications (image, text, etc.).
Or entirely different types of applications can be built.
We will model different types of applications by different parameterizations.
Different combinations are suitable for different types of applications.
Many established businesses have long and complex histories of deploying different types of applications.
We use virtual queues for the two different types of traffic, real-time traffic and non-real-time traffic, whose packets are labeled as different flow number accordingly.
In this case, the slightly unbalanced fish topology adopted in the CN enables traffic engineering applications since different types of traffic may follow different paths in the CN.
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