# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Scheduler
#### 3.2.3 Lesson Learned  
In this subsection, we introduce some multipath TCP schedulers. We categorized the schedulers into four groups here, including simple scheduler, path quality estimation-based scheduler, delay difference-based scheduler, and learning-based scheduler. Simple schedulers, such as RR and LowRTT, simply distribute packets in a circular order or based on RTT. However, these methods have limitations in irregular network conditions such as lossy networks or heterogeneous networks. In order to sufficiently utilize the network resources by keeping the paths longer at a saturation point, the path quality estimation-based scheduler estimates the bandwidth or transmission time on all available paths before distributing data. However, the path estimation-based schedulers face some challenges if the network condition changes very quickly. This is because the estimated path states will occur deviation as time goes on. Although some efforts have been made to discover network changes and make adjustment on the scheduling strategy accordingly, it is still unable to predict the network changes in advance or implement scheduling synchronously with network changes by a path quality estimation-based scheduler. On the other hand, the delay difference-based schedulers are for minimizing the delay gap of different paths. By doing this, the data packets that transmitted on different paths can arrive at the receiver buffer simultaneously, avoiding packet reordering. However, this method has the disadvantage of sacrificing the bandwidth of fast paths. Meanwhile, in order to solve the delay difference problem for short flows, there is the possibility of disabling the slow path when the delay difference between different paths is huge. However, it will result in a lower usage rate of multiple paths. The aforementioned approaches are pre-determined schedulers and are hard to satisfy the performance requirements of recent new technologies such as vehicular networks. In order to meet the requirements of highly mobile networks, some learning-based schedulers are utilized to dynamically adjust the scheduling policy according to the changing network environments. The learning-based approaches have explored a new area for MPTCP scheduling. However, they also have some problems. First, the learning-based approaches need some time to train the model before convergence. Second, the performance improvement is not significant when applying deep reinforcement learning in some scenarios. Furthermore, sometimes a learning-based scheduler is outperformed by fundamental schedulers under some settings. For future works, firstly, it is a promising way to consider a combined design, including the combination of scheduler with congestion control. Second, while most AI-based schedulers are trained and tested based on simulation data, it is urgent to test their performance in real-world network scenarios to verify their effectiveness. It is also necessary to further review the state and reward in order to ensure the validity of the learning model. Thirdly, it could be a solution to overcome the training delay by combining an AI-based