1 research outputs found
Confucius Queue Management: Be Fair But Not Too Fast
When many users and unique applications share a congested edge link (e.g., a
home network), everyone wants their own application to continue to perform well
despite contention over network resources. Traditionally, network engineers
have focused on fairness as the key objective to ensure that competing
applications are equitably and led by the switch, and hence have deployed fair
queueing mechanisms. However, for many network workloads today, strict fairness
is directly at odds with equitable application performance. Real-time streaming
applications, such as videoconferencing, suffer the most when network
performance is volatile (with delay spikes or sudden and dramatic drops in
throughput). Unfortunately, "fair" queueing mechanisms lead to extremely
volatile network behavior in the presence of bursty and multi-flow applications
such as Web traffic. When a sudden burst of new data arrives, fair queueing
algorithms rapidly shift resources away from incumbent flows, leading to severe
stalls in real-time applications. In this paper, we present Confucius, the
first practical queue management scheme to effectively balance fairness against
volatility, providing performance outcomes that benefit all applications
sharing the contended link. Confucius outperforms realistic queueing schemes by
protecting the real-time streaming flows from stalls in competing with more
than 95% of websites. Importantly, Confucius does not assume the collaboration
of end-hosts, nor does it require manual parameter tuning to achieve good
performance