We report on experiences with Swift congestion control in Google datacenters. Swift targets
an end-to-end delay by using AIMD control, with pacing under extreme congestion. With …

Small RTTs (~ tens of microseconds), bursty flow arrivals, and a large number of concurrent
flows (thousands) in datacenters bring fundamental challenges to congestion control as they …

Datacenter transports aim to deliver low latency messaging together with high throughput.
We show that simple packet delay, measured as round-trip times at hosts, is an effective …

Increasingly stringent throughput and latency requirements in datacenter networks demand
fast and accurate congestion control. We observe that the reaction time and accuracy of …

Cloud data centers host diverse applications, mixing in the same network a plethora of
workflows that require small predictable latency with others requiring large sustained …

Traditional measures of network goodness—goodput, quality of service, fairness—are
expressed in terms of bandwidth. Network latency has rarely been a primary concern …

Rapid convergence to a desired allocation of network resources to endpoint traffic is a
difficult problem. The reason is that congestion control decisions are distributed across the …

Cloud services are deployed in datacenters connected though high-bandwidth Wide Area
Networks (WANs). We find that WAN traffic negatively impacts the performance of datacenter …

The soft real-time nature of large scale web applications in today's datacenters, combined
with their distributed workflow, leads to deadlines being associated with the datacenter …

The nature of congestion feedback largely governs the behavior of congestion control. In
datacenter networks, where RTTs are in hundreds of microseconds, accurate feedback is …