gVisor is designed to provide a secure, virtualized environment while preserving key benefits of containerization, such as small fixed overheads and a dynamic resource footprint. For containerized infrastructure, this can provide a turn-key solution for sandboxing untrusted workloads: there are no changes to the fundamental resource model.
gVisor imposes runtime costs over native containers. These costs come in two forms: additional cycles and memory usage, which may manifest as increased latency, reduced throughput or density, or not at all. In general, these costs come from two different sources.
First, the existence of the Sentry means that additional memory will be required, and application system calls must traverse additional layers of software. The design emphasizes security and therefore we chose to use a language for the Sentry that provides benefits in this domain but may not yet offer the raw performance of other choices. Costs imposed by these design choices are structural costs.
Second, as gVisor is an independent implementation of the system call surface, many of the subsystems or specific calls are not as optimized as more mature implementations. A good example here is the network stack, which is continuing to evolve but does not support all the advanced recovery mechanisms offered by other stacks and is less CPU efficient. This is an implementation cost and is distinct from structural costs. Improvements here are ongoing and driven by the workloads that matter to gVisor users and contributors.
This page provides a guide for understanding baseline performance, and calls out distinct structural costs and implementation costs, highlighting where improvements are possible and not possible.
While we include a variety of workloads here, it’s worth emphasizing that gVisor may not be an appropriate solution for every workload, for reasons other than performance. For example, a sandbox may provide minimal benefit for a trusted database, since user data would already be inside the sandbox and there is no need for an attacker to break out in the first place.
All data below was generated using the benchmark tools repository, and the machines under test are uniform Google Compute Engine Virtual Machines (VMs) with the following specifications:
Machine type: n1-standard-4 (broadwell) Image: Debian GNU/Linux 9 (stretch) 4.19.0-0 BootDisk: 2048GB SSD persistent disk
Through this document,
runsc is used to indicate the runtime provided by
gVisor. When relevant, we use the name
runsc-platform to describe a specific
Except where specified, all tests below are conducted with the
ptrace platform works everywhere and does not require hardware
virtualization or kernel modifications but suffers from the highest structural
costs by far. This platform is used to provide a clear understanding of the
performance model, but in no way represents an ideal scenario. In the future,
this guide will be extended to bare metal environments and include additional
gVisor does not introduce any additional costs with respect to raw memory accesses. Page faults and other Operating System (OS) mechanisms are translated through the Sentry, but once mappings are installed and available to the application, there is no additional overhead.
The above figure demonstrates the memory transfer rate as measured by
The Sentry provides an additional layer of indirection, and it requires memory in order to store state associated with the application. This memory generally consists of a fixed component, plus an amount that varies with the usage of operating system resources (e.g. how many sockets or files are opened).
For many use cases, fixed memory overheads are a primary concern. This may be because sandboxed containers handle a low volume of requests, and it is therefore important to achieve high densities for efficiency.
The above figure demonstrates these costs based on three sample applications.
This test is the result of running many instances of a container (50, or 5 in
the case of redis) and calculating available memory on the host before and
afterwards, and dividing the difference by the number of containers. This
technique is used for measuring memory usage over the
usage_in_bytes value of
the container cgroup because we found that some container runtimes, other than
runsc, do not use an individual container cgroup.
The first application is an instance of
sleep: a trivial application that does
nothing. The second application is a synthetic
node application which imports
a number of modules and listens for requests. The third application is a similar
ruby application which does the same. Finally, we include an
redis storing approximately 1GB of data. In all cases, the sandbox
itself is responsible for a small, mostly fixed amount of memory overhead.
gVisor does not perform emulation or otherwise interfere with the raw execution of CPU instructions by the application. Therefore, there is no runtime cost imposed for CPU operations.
The above figure demonstrates the
sysbench measurement of CPU events per
second. Events per second is based on a CPU-bound loop that calculates all prime
numbers in a specified range. We note that
runsc does not impose a performance
penalty, as the code is executing natively in both cases.
This has important consequences for classes of workloads that are often
CPU-bound, such as data processing or machine learning. In these cases,
will similarly impose minimal runtime overhead.
For example, the above figure shows a sample TensorFlow workload, the convolutional neural network example. The time indicated includes the full start-up and run time for the workload, which trains a model.
Some structural costs of gVisor are heavily influenced by the platform choice, which implements system call interception. Today, gVisor supports a variety of platforms. These platforms present distinct performance, compatibility and security trade-offs. For example, the KVM platform has low overhead system call interception but runs poorly with nested virtualization.
The above figure demonstrates the time required for a raw system call on various platforms. The test is implemented by a custom binary which performs a large number of system calls and calculates the average time required.
This cost will principally impact applications that are system call bound, which tend to be high-performance data stores and static network services. In general, the impact of system call interception will be lower the more work an application does.
redis is an application that performs relatively little work in
userspace: in general it reads from a connected socket, reads or modifies some
data, and writes a result back to the socket. The above figure shows the results
of running comprehensive set of benchmarks. We can see that
small operations impose a large overhead, while larger operations, such as
LRANGE, where more work is done in the application, have a smaller relative
Some of these costs above are structural costs, and
redis is likely to
remain a challenging performance scenario. However, optimizing the
platform will also have a dramatic
For many use cases, the ability to spin-up containers quickly and efficiently is important. A sandbox may be short-lived and perform minimal user work (e.g. a function invocation).
The above figure indicates how total time required to start a container through
Docker. This benchmark uses three different applications. First, an
alpine Linux-container that executes
true. Second, a
node application that
loads a number of modules and binds an HTTP server. The time is measured by a
successful request to the bound port. Finally, a
ruby application that
similarly loads a number of modules and binds an HTTP server.
Note: most of the time overhead above is associated Docker itself. This is evident with the empty
runcbenchmark. To avoid these costs with
runsc, you may also consider using
runsc domode or invoking the OCI runtime directly.
Networking is mostly bound by implementation costs, and gVisor’s network stack is improving quickly.
While typically not an important metric in practice for common sandbox use
iperf is a common microbenchmark used to measure raw
The above figure shows the result of an
iperf test between two instances. For
the upload case, the specified runtime is used for the
iperf client, and in
the download case, the specified runtime is the server. A native runtime is
always used for the other endpoint in the test.
The above figure shows the result of simple
ruby web services that
render a template upon receiving a request. Because these synthetic benchmarks
do minimal work per request, much like the
redis case, they suffer from high
overheads. In practice, the more work an application does the smaller the impact
of structural costs become.
Some aspects of file system performance are also reflective of implementation costs, and an area where gVisor’s implementation is improving quickly.
In terms of raw disk I/O, gVisor does not introduce significant fundamental overhead. For general file operations, gVisor introduces a small fixed overhead for data that transitions across the sandbox boundary. This manifests as structural costs in some cases, since these operations must be routed through the Gofer as a result of our Security Model, but in most cases are dominated by implementation costs, due to an internal Virtual File System (VFS) implementation that needs improvement.
The above figures demonstrate the results of
fio for reads and writes to and
from the disk. In this case, the disk quickly becomes the bottleneck and
dominates other costs.
The above figure shows the raw I/O performance of using a
tmpfs mount which is
sandbox-internal in the case of
runsc. Generally these operations are
similarly bound to the cost of copying around data in-memory, and we don’t see
the cost of VFS operations.
The high costs of VFS operations can manifest in benchmarks that execute many
such operations in the hot path for serving requests, for example. The above
figure shows the result of using gVisor to serve small pieces of static content
with predictably poor results. This workload represents
apache serving a
single file sized 100k from the container image to a client running
ApacheBench with varying levels of concurrency. The high overhead comes
principally from the VFS implementation that needs improvement, with several
internal serialization points (since all requests are reading the same file).
Note that some of some of network stack performance issues also impact this
For benchmarks that are bound by raw disk I/O and a mix of compute, file system
operations are less of an issue. The above figure shows the total time required
ffmpeg container to start, load and transcode a 27MB input video.