# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 2 Related Works
### Multipath Data-Scheduling Strategy  
Multipath data scheduling refers to distributing traffic to different network paths according to specific strategies during transmission to achieve load balancing. The round-robin scheduling strategy distributes the application layer's data to each sub-path in a polling manner, ignoring the path state differences such as bandwidth and RTT. This strategy performs well when the state differences among the sub-paths are not significant; however, when the state differences are significant, it may lead to a decrease in the overall throughput. In contrast, the weighted round-robin scheduling strategy takes into account the status differences between paths. The Lowest RTT First algorithm [26] is a typical weighted round-robin scheduling strategy that prioritizes multiple available paths according to RTT. The smaller the RTT of the path, the higher its priority. For instance, Multipath TCP (MPTCP) [13] integrates the Lowest RTT First algorithm in the Linux kernel. Considering that the transmission paths of data packets and acknowledge packets are usually asymmetric, RTT cannot accurately reflect the real path state. Therefore, Forward-Delay-Based Packet Scheduling (FDPS) [27] prioritizes the path by measuring the forward delay of the data packets, thus improving the accuracy of the path state estimation. The redundant scheduling strategy [28] distributes packets repeatedly to each sub-path, alleviating the out-of-order problem to a certain extent. However, the same packets are transmitted over different paths, which affects the resource utilization of multiple paths. The hash scheduling strategy takes information such as the source IP address, destination IP address, source port and destination port as inputs to a hash function and selects the path based on the output result. For instance, ECMP applies a hash scheduling strategy [29]. However, the hash scheduling strategy is prone to hash collision, making it difficult to achieve load balancing. The prediction-based scheduling strategy calculates the traffic distribution ratio by predicting the congestion degree or arrival time of each path. Forward prediction scheduling (FPS) [30] can reduce the number of out-of-order packets by predicting the arrival time of each sub-path. In [31], the authors proposed Fine-Grained Forward-Prediction-Based Dynamic Packet Scheduling (F2 PDPS), which allocates more packets to under-scheduled sub-flows by estimating the number of packets being transmitted by the sub-flows. Additionally, many researchers have proposed using a central controller to collect network topology and path congestion information and allocate data flow to each transmission path. [PERSON] et al. [32] proposed a flow-level congestion-aware load balancing method which dynamically adjusts path weights based on bandwidth utilization and prioritizes flow allocation to the path with the minimum weight. In [33], the authors proposed to use a central controller to determine the transmission path for each packet to improve network utilization and reduce queuing delay.