Google Cloud networking in-depth: How Andromeda 2.2 enables high-throughput VMs

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Here at Google Cloud, we’ve always aimed to provide great network bandwidth for Compute Engine VMs, thanks in large part to our custom Jupiter network fabric and Andromeda virtual network stack. During Google Cloud Next ’19, we improved that bandwidth even further by doubling the maximum network egress data rate to 32 Gbps for common VM types. We also announced VMs with up to 100 Gbps bandwidth on V100 and T4 GPU accelerator platforms–all without raising prices or requiring you to use premium VMs.

Specifically, for any Skylake or newer VM with at least 16 vCPUs, we raised the egress bandwidth cap to 32 Gbps for same-zone VM-to-VM traffic; this capability is now generally available. This includes n1-ultramem VMs, which provide more compute resources and memory than any other Compute Engine VM instance type. There is no additional configuration needed to get that 32 Gbps throughput.

Meanwhile, 100 Gbps Accelerator VMs are in alpha, soon in beta. Any VM with eight V100 or four T4 GPUs attached will have bandwidth caps raised to 100 Gbps.

These high-throughput VMs are ideal for running compute-intensive workloads that also need a lot of networking bandwidth. Some key applications and workloads that can leverage these high-throughput VMs are:

  • High-performance computing applications, batch processing, scientific modeling

  • High-performance web servers

  • Virtual network appliances (firewalls, load balancers)

  • Highly scalable multiplayer gaming

  • Video encoding services

  • Distributed analytics

  • Machine learning and deep learning

In addition, services built on top of Compute Engine like CloudSQL, Cloud Filestore and some partner solutions can leverage 32 Gbps throughput already.

One use case that is particularly network- and compute-intensive is distributed machine learning (ML). To train large datasets or models, ML workloads use a distributed ML framework, e.g., TensorFlow. The dataset is divided and trained by separate workers, which exchange model parameters with each other. These ML jobs consume substantial network bandwidth due to large model size and frequent data exchanges among workers. Likewise, the compute instances that run the worker nodes create high throughput requirements for VMs and the fabric serving the VMs. One customer, a large chip manufacturer, leverages 100 Gbps GPU-based VMs to run these massively parallel ML jobs, while another customer uses our 100 Gbps GPU machines to test a massively parallel seismic analysis application.

Making it all possible: Jupiter and Andromeda
Our highly-scalable Jupiter network fabric and high-performance, flexible Andromeda virtual network stack are the same technologies that power Google’s internal infrastructure and services.

Jupiter provides Google with tremendous bandwidth and scale. For example, Jupiter fabrics can deliver more than 1 Petabit/sec of total bisection bandwidth. To put this in perspective, this is enough capacity for 100,000 servers to exchange information at a rate of 10 Gbps each, or enough to read the entire scanned contents of the Library of Congress in less than 1/10th of a second.

Andromeda, meanwhile, is a Software Defined Networking (SDN) substrate for our network virtualization platform, acting as the orchestration point for provisioning, configuring, and managing virtual networks and in-network packet processing. Andromeda lets us share Jupiter networks for many different uses, including Compute Engine and bandwidth-intensive products like Cloud BigQuery and Cloud Bigtable.

Since we last blogged about Andromeda, we launched Andromeda 2.2. Among other infrastructure improvements, Andromeda 2.2 features increased performance and improved performance isolation through the use of hardware offloads, enabling you to achieve the network performance you want, even in a multi-tenant environment.

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