We propose an adaptive fractional window increasing algorithm (AFW) to improve the performance of the fractional window increment (FeW) in (Nahm et al., 2005). AFW fully utilizes the bandwidth when the network is idle...We propose an adaptive fractional window increasing algorithm (AFW) to improve the performance of the fractional window increment (FeW) in (Nahm et al., 2005). AFW fully utilizes the bandwidth when the network is idle, and limits the op-erating window when the network is congested. We evaluate AFW and compare the total throughput of AFW with that of FeW in different scenarios over chain, grid, random topologies and with hybrid traffics. Extensive simulation through ns2 shows that AFW obtains 5% higher throughput than FeW, whose throughput is significantly higher than that of TCP-Newreno, with limited modi-fications.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 60625103, 60702046 and 60832005)the Doctoral Fund of MOE of China (No. 20070248095)+3 种基金the China International Science and Technology Cooperation Program (No. 2008DFA11630)the Shanghai Science and Technology PUJIANG Talents Project (No. 08PJ14067)Innovation Key Project (No. 08511500400)the Qualcomm Research Grant
文摘We propose an adaptive fractional window increasing algorithm (AFW) to improve the performance of the fractional window increment (FeW) in (Nahm et al., 2005). AFW fully utilizes the bandwidth when the network is idle, and limits the op-erating window when the network is congested. We evaluate AFW and compare the total throughput of AFW with that of FeW in different scenarios over chain, grid, random topologies and with hybrid traffics. Extensive simulation through ns2 shows that AFW obtains 5% higher throughput than FeW, whose throughput is significantly higher than that of TCP-Newreno, with limited modi-fications.
