Research On Congestion Control Algorithm For Big Data Transmission

With the rapid development of the Internet,more and more traffic is carried in the network,and the role of congestion control algorithms is becoming more and more prominent.A good congestion control algorithm is an important guarantee for efficient and stable data transmission.In recent years in the field of cyberspace security,hot research contents such as data off-site disaster recovery and backup,network security situational awareness,and network threat intelligence sharing are involved in big data transmission.The big data transmission scenarios put forward higher requirements on congestion control algorithms in terms of convergence performance,retransmission and adaptability,which cannot be met by existing congestion control algorithms.The current BBR(Bottleneck Bandwidth and Round-trip propagation time)algorithm and its derivatives have poor convergence performance and retransmission in big data transmission scenarios,while the algorithms that introduce fine-grained network information feedback are not suitable for long-distance transmission and often rely on dedicated hardware,which increases the cost and difficulty of deployment.To address the above problems,this thesis optimizes the end-system and endnetwork collaboration in two directions,respectively.At the end side,this thesis establishes a convergence behavior model of BBR flows in the probing bandwidth stage and proposes a congestion control algorithm considering network cache resources;meanwhile,it introduces fine-grained network feedback information in the big data transmission scenario and proposes an event-driven end-network cooperative congestion control algorithm.The specific research work is as follows:(1)In this thesis,a BBR-based gain-adaptive congestion control algorithm is proposed.Based on the problem that BBR and its derived algorithms do not consider network cache resources,a convergence domain model is proposed to describe the convergence behavior of each flow of the BBR algorithm in the probing bandwidth phase.Then the problem of fast convergence of each BBR flow without exceeding the buffer is transformed into an optimization problem,and the initial value strategy for the transmit rate gain is obtained after solving.Then,the network congestion is further considered,and a modulation factor is introduced into the sending rate gain acquisition strategy to further reduce the sending rate gain to mitigate the congestion.(2)This thesis proposes an event-driven end-network cooperative congestion control algorithm.The fine-grained network information feedback is introduced to the congestion control in the big data transmission scenario while reducing the requirements on the network equipment.The network state information collection and event-triggered reporting mechanism are implemented on the network devices,and the network congestion degree division and phased congestion control strategy are implemented on the end system.Considering the dynamic change characteristics of the network,a window adjustment strategy based on cache gradient is designed for window control,which combines the cache occupancy ratio and cache occupancy change to predict the subsequent cache occupancy size,so as to maximize the utilization of bandwidth resources.In this thesis,we have conducted extensive experiments and demonstrated the superior performance of the BBR-based gain adaptive congestion control algorithm and the event-driven end-network cooperative congestion control algorithm by comparing performance analysis with benchmark algorithms to achieve higher transmission speed,lower retransmission and faster convergence in large data transmission scenarios.