This is part of the series highlighting some notable publications by Googlers.
At Google, we operate large datacenters containing clusters of servers, networking switches, and more. While this gear costs a lot of money, an increasingly important cost -- both in terms of dollars and environmental impact -- is the electricity that drives the computing clusters and the cooling infrastructure. Since our clusters often do not run at full utilization, Google recently put forth a call to industry and researchers to develop energy proportional computer systems. With such systems, the power consumed by our clusters would be directly proportional to utilization. Servers consume the most electricity, and therefore researchers have responded to Google’s call by focusing their attention towards servers. As the servers become increasingly energy proportional, however, the “always on” network fabric that connects servers together will consume an increasing fraction of datacenter power unless it too becomes energy proportional.
In a paper recently published at the International Symposium on Computer Architecture (ISCA), we push further towards the goal of energy-proportional computing by focusing on the energy usage of high-bandwidth, highly-scalable cluster networking fabrics. This research considers a broad set of architectural and technological solutions to optimize energy usage without sacrificing performance. First, we show how the Flattened Butterfly network topology uses less power since it uses less switching chips and fewer links than a comparable-performance network built using the more conventional Fat Tree topology. Second, our approach takes advantage of the observation that when network demand is low, we can reduce the speed at which links transmit data. We show via simulation, that by tuning the speeds of the links very rapidly, we can reduce power consumption with little impact on performance. Finally, our research is a further call to action for the academic and industry research communities to make energy efficiency, and energy proportionality in particular, a first-class citizen in networking research. Put together, our proposed techniques can reduce energy cost for typical Google workloads seen in our production datacenters by millions of dollars!
