This paper presents our design and experience with Andromeda,
Google Cloud Platform’s network virtualization
stack. Our production deployment poses several challenging
requirements, including performance isolation among
customer virtual networks, scalability, rapid provisioning
of large numbers of virtual hosts, bandwidth and latency
largely indistinguishable from the underlying hardware,
and high feature velocity combined with high availability.
Andromeda is designed around a flexible hierarchy of
flow processing paths. Flows are mapped to a programming
path dynamically based on feature and performance
requirements. We introduce the Hoverboard programming
model, which uses gateways for the long tail of low bandwidth
flows, and enables the control plane to program
network connectivity for tens of thousands of VMs in
seconds. The on-host dataplane is based around a highperformance
OS bypass software packet processing path.
CPU-intensive per packet operations with higher latency
targets are executed on coprocessor threads. This architecture
allows Andromeda to decouple feature growth from
fast path performance, as many features can be implemented
solely on the coprocessor path. We demonstrate
that the Andromeda datapath achieves performance that is
competitive with hardware while maintaining the flexibility
and velocity of a software-based architecture.
We present the design of Espresso, Google’s SDN-based Internet
peering edge routing infrastructure. This architecture grew out of a
need to exponentially scale the Internet edge cost-effectively and to
enable application-aware routing at Internet-peering scale. Espresso
utilizes commodity switches and host-based routing/packet process-
ing to implement a novel fine-grained traffic engineering capability.
Overall, Espresso provides Google a scalable peering edge that is
programmable, reliable, and integrated with global traffic systems.
Espresso also greatly accelerated deployment of new networking
features at our peering edge. Espresso has been in production for
two years and serves over 22% of Google’s total traffic to the Inter-
Amin Vahdat keynotes ONS again…
Today, we gave a keynote presentation at the Open Networking Summit, where we shared details about Espresso, Google’s peering edge architecture—the latest offering in our Software Defined Networking (SDN) strategy. Espresso has been in production for over two years and routes 20 percent of our total traffic to the internet—and growing. It’s changing the way traffic is directed at the peering edge, delivering unprecedented scale, flexibility and efficiency.
Today’s network control and management traffic are limited by
their reliance on existing data networks. Fate sharing in this context
is highly undesirable, since control traffic has very different availability
and traffic delivery requirements. In this paper, we explore
the feasibility of building a dedicated wireless facilities network for
data centers. We propose Angora, a low-latency facilities network
using low-cost, 60GHz beamforming radios that provides robust
paths decoupled from the wired network, and flexibility to adapt to
workloads and network dynamics. We describe our solutions to address
challenges in link coordination, link interference and network
failures. Our testbed measurements and simulation results show
that Angora enables large number of low-latency control paths to
run concurrently, while providing low latency end-to-end message
delivery with high tolerance for radio and rack failures.
Predictably sharing the network is critical to achieving
high utilization in the datacenter. Past work has focussed
on providing bandwidth to endpoints, but often
we want to allocate resources among multi-node services.
In this paper, we present Parley, which provides
service-centric minimum bandwidth guarantees, which
can be composed hierarchically. Parley also supports
service-centric weighted sharing of bandwidth in excess
of these guarantees. Further, we show how to configure
these policies so services can get low latencies even at
high network load. We evaluate Parley on a multi-tiered
oversubscribed network connecting 90 machines, each
with a 10Gb/s network interface, and demonstrate that
Parley is able to meet its goals.
Abstract— We show that the performance of existing
fault localization algorithms differs markedly for different
networks; and no algorithm simultaneously provides
high localization accuracy and low computational overhead.
We develop a framework to explain these behaviors
by anatomizing the algorithms with respect to six
important characteristics of real networks, such as uncertain
dependencies, noise, and covering relationships. We
use this analysis to develop Gestalt, a new algorithm that
combines the best elements of existing ones and includes
a new technique to explore the space of fault hypotheses.
We run experiments on three real, diverse networks. For
each, Gestalt has either significantly higher localization
accuracy or an order of magnitude lower running time.
For example, when applied to the Lync messaging system
that is used widely within corporations, Gestalt localizes
faults with the same accuracy as Sherlock, while
reducing fault localization time from days to 23 seconds.