The Impact of Large-Data Transfers in Shared Wide-Area Networks: An Empirical Study

Anvari, Hamidreza; Lu, Paul

doi:10.1016/j.procs.2017.05.211

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…The results are depicted in Figures 13b and 14b for Overlay-1 and Overlay-2 networks, respectively. We included three configurations of UDP(1,2), UDP (1,5), and UDP (1,10), where the UDP-based traffic cycles ON every 2, 5, and 10 s, respectively, transferring a 1 s burst of UDP traffic before cycling OFF. This scenario further uncovers the sensitivity of GridFTP traffic to the bursty UDP cross traffic, experiencing a drop in throughput as low as 1/10.…”

Section: Impact Of Cross Traffic On High-performance Toolsmentioning

confidence: 99%

Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection

Anvari

2021

Computers

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We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in up to four times higher throughput in some key cases. However, OPS for the foreground traffic (e.g., TCP CUBIC, TCP BBR, UDT) depends on knowledge about the network protocols used by the background traffic (i.e., other users). Therefore, we build and empirically evaluate several machine-learned (ML) classifiers, trained on local round-trip time (RTT) time-series data gathered using active probing, to recognize the mix of network protocols in the background with an accuracy of up to 0.96.

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Section: Impact Of Cross Traffic On High-performance Toolsmentioning

confidence: 99%