Due to the asynchronous and distributed nature of the data plane, the transition from the initial to final state may result in serious transient congestion in software-defined networking (SDN). Moreover, with the rapid development of ultra-low latency network in data-centers, the control loop between the control and data plane becomes much longer than the ultra-low latency communication in the data plane. In this case, traffic surges significantly in the data plane during the network reconfiguration process, and it becomes harder for the SDN controllers to manipulate the update operations as they expect. In this paper, we consider the traffic variation during the network update and model it as a novel minimum demand violation problem (MDVP). Later, we prove its hardness, and propose a heuristic approximation algorithm to approach the optimal result. Our method brings flexibility for network operators to make a trade-off between the network congestion and update speed. Experiments show that our method can halve the intermediate network states and reduce the demand violation ratio by 36.7 % compared to the state-of-the-art. INDEX TERMS Software-defined networking (SDN), network update, ultra-low latency network, minimum demand violation problem (MDVP).
Musical emotion is important for the listener's cognition. A smooth emotional expression generated through listening to music makes driving a car safer. Music has become more diverse and prolific with rapid technological developments. However, the cost of music production remains very high. At present, because the cost of music creation and the playing copyright are still very expensive, the music that needs to be listened to while driving can be executed by the way of automated composition of AI to achieve the purpose of driving safety and convenience. To address this problem, automated AI music composition has gradually gained attention in recent years. This study aims to establish an automated composition system that integrates music, emotion, and machine learning. The proposed system takes a music database with emotional tags as input, and deep learning trains the conditional variational autoencode generative adversarial network model as a framework to produce musical segments corresponding to the specified emotions. The system takes the music database with emotional tags as input, and deep learning trains the CVAE-GAN model as the framework to produce the music segments corresponding to the specified emotions. Participants listen to the results of the system and judge whether the music corresponds to their original emotion.
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