APS: Adaptive Packet Sizing for Efficient End-to-End Network Transmission

Much effort has been devoted to improving the performance of network transmission. Yet, the impact of packet size which is limited by the 1500-byte maximum transmission unit (MTU) has not received adequate attention. Through comprehensive experiments, we find that jumbo frames which are commonly used as an alternate do not always yield the best performance under different transmission situations.In this paper, we elaborate on the limitations of the regular and jumbo frames and analyze how packet sizes affect network performance. Based on these, we present Adaptively Packet Sizing (APS), a dynamic packet size adjustment method that can be easily integrated into existing window-based congestion control algorithms. APS utilizes a machine learning method to predict the optimal packet size, which can minimize flow completion time (FCT) according to the instantaneous network condition. Besides, a packet size based priority mechanism is proposed to further improve the performance. We implement APS in both simulation and testbed environments. APS reduces the FCT by up to 50% and gains better performance in scenarios with various loss rates.