6th International ICST Conference on Communications and Networking in China

Research Article

Modeling TCP Performance in Reordering and Lossy Networks

  • @INPROCEEDINGS{10.1109/ChinaCom.2011.6158263,
        author={wei xu and yinlong xu and xiaohu wu},
        title={Modeling TCP Performance in Reordering and Lossy Networks},
        proceedings={6th International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2012},
        month={3},
        keywords={tcp performance packet lost networks packet delay networks slightly out of orde},
        doi={10.1109/ChinaCom.2011.6158263}
    }
    
  • wei xu
    yinlong xu
    xiaohu wu
    Year: 2012
    Modeling TCP Performance in Reordering and Lossy Networks
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2011.6158263
wei xu1,*, yinlong xu1, xiaohu wu1
  • 1: ustc
*Contact email: xuweihf@mail.ustc.edu.cn

Abstract

Modeling TCP performance is an important issue in computer networks and plays an important rule to improve network performance. There are two types of packet transmission errors in network, packet loss and delay. Different errors will induce to different influence on network performance. Most of the recent researches only consider modeling TCP in packet lost networks. This paper first studies the problem of modeling TCP throughput in packet delay networks. Firstly, we give a sufficient and necessary condition to identify whether a data packet flow is slightly out of order or in order. Based on this condition, the probability that a data packet flow is slightly out of order or in order is analyzed. Finally, TCP throughput from the obtained probability of data packet flow is slightly out of order or in order is evaluated. Numerical results indicate that the difference between the probability of data packet flow is slightly out of order or in order of our model and which in real networks is tiny, and compared with those existing models which do not consider packet delay, our model are more accurate in maximum congestion window prediction and TCP throughput prediction.