Modeling and predicting starlink throughput with fine-grained burst characterization

Abstract

Leveraging a dataset of almost half a billion packets with high-precision packet times and sizes, we extract characteristics of the bursts emitted over Starlink’s Ethernet interface. The structure of these bursts directly reflects the physical layer reception of OFDMA frames on the satellite link. We study these bursts by analyzing their rates, and thus indirectly also the transition between different physical layer rates. The results highlight that there is definitive structure in the transition behavior, and we note specific behaviors such as particular transition steps associated with rate switching, and that rate switching occurs mainly to neighboring rates. We also study the joint burst rate and burst duration transitions, noting that transitions occur mainly within the same rate, and that changes in burst duration are often performed with an intermediate short burst in-between. Furthermore, we examine the configurations of the three factors burst rate, burst duration, and inter-burst silent time, which together determine the effective throughput of a Starlink connection. We perform pattern mining on these three factors, and we use the patterns to construct a dynamic N-gram model predicting the characteristics of the next upcoming burst, and by extension, the short-term future throughput. We further train a Deep Learning time-series model which shows improved prediction performance.