Abstract
Mobile broadband networks, although increasingly popular, suffer large fluctuations in performance. Download speeds can drop by 50% or more during peak hours. Hence, understanding and dissecting the causes of these fluctuations is central to improving current and future networks. In this paper, we propose a congestion detection and localisation method, Q-TSLP, that combines and extends the two state-of-the-art congestion detection tools: Q-Probe and TSLP. Q-Probe monitors patterns in packet arrivals, while TSLP tracks shifts in RTT to detect bottleneck at different segments of an end-to-end path. QProbe can attribute congestion, at a very coarse level, to either radio or non-radio related. TSLP on the other hand cannot pinpoint radio related congestion. Q-TSLP provides a per-hop congestion attribution thus addressing these limitations. To this end, we build two small scale LTE testbeds and experiment with a series of congestion scenarios. These controlled experiments show that apart from correct congestion localisation to finer granularity, the detection accuracy improves significantly with Q-TSLP, up to 100% in some cases. We then run a three-month long measurement campaign of congestion over two commercial operators in Norway. Overall, we run 17 million tests from a large number of geographically distributed probes. We find that both operators suffer congestion at different parts of the network. Our findings indicate that apart from mobile radio access, a non-trivial fraction of cases is related to congested mobile operator and Internet paths beyond the mobile network core. These findings hint that operators may need significant infrastructure upgrades to cope with potential 5G traffic volumes.
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