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| -rw-r--r-- | content/_index.html | 4 | ||||
| -rw-r--r-- | content/docs/architecture_guide/performance.md | 45 |
2 files changed, 27 insertions, 22 deletions
diff --git a/content/_index.html b/content/_index.html index 70c863bc4..9e82ea98d 100644 --- a/content/_index.html +++ b/content/_index.html @@ -7,7 +7,7 @@ description = "A container sandbox runtime focused on security, efficiency, and {{< blocks/cover image_anchor="top" height="auto" color="primary" title="gVisor" >}} <div class="mx-auto"> <p class="lead">A container sandbox runtime focused on <strong>security</strong>, <strong>efficiency</strong>, and <strong>ease of use</strong>.</p> - <a class="btn btn-lg btn-primary mr-3 mb-4" href="./docs/user_guide/docker/" >Quick Start<i class="fas fa-arrow-alt-circle-right ml-2"></i></a> + <a class="btn btn-lg btn-primary mr-3 mb-4" href="./docs/user_guide/docker/">Quick Start<i class="fas fa-arrow-alt-circle-right ml-2"></i></a> <a class="btn btn-lg btn-secondary mr-3 mb-4" href="https://github.com/google/gvisor" rel="noopener">GitHub <i class="fab fa-github ml-2"></i></a> </div> {{< /blocks/cover >}} @@ -41,7 +41,7 @@ Read the [documentation](./docs/) to understand gVisor, its architecture and tra {{% /blocks/feature %}} {{% blocks/feature icon="fas fa-code-branch" title="Contribute to gVisor" %}} -Anyone is welcome to be a gVisor contributor. Please check out the [community information](./docs/community) to get started. +Anyone is welcome to be a gVisor contributor. Please check out the [community information](./docs/community/) to get started. {{% /blocks/feature %}} {{% blocks/feature icon="fab fa-github" title="Give Feedback" %}} diff --git a/content/docs/architecture_guide/performance.md b/content/docs/architecture_guide/performance.md index 8129ce353..6a14f31e8 100644 --- a/content/docs/architecture_guide/performance.md +++ b/content/docs/architecture_guide/performance.md @@ -2,6 +2,7 @@ title = "Performance Guide" weight = 30 +++ + 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 @@ -35,7 +36,7 @@ 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 +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. ## Methodology @@ -44,11 +45,9 @@ All data below was generated using the [benchmark tools][benchmark-tools] repository, and the machines under test are uniform [Google Compute Engine][gce] 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 -``` + 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 @@ -69,7 +68,7 @@ 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. -{{< graph id="sysbench-memory" url="/performance/sysbench-memory.csv" title="perf.py sysbench.memory --runtime=runc --runtime=runsc" >}} +{{< graph id="sysbench-memory" url="/performance/sysbench-memory.csv" title="perf.py sysbench.memory --runtime=runc --runtime=runsc" >}} The above figure demonstrates the memory transfer rate as measured by `sysbench`. @@ -85,7 +84,7 @@ 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. -{{< graph id="density" url="/performance/density.csv" title="perf.py density --runtime=runc --runtime=runsc" log="true" y_min="100000" >}} +{{< graph id="density" url="/performance/density.csv" title="perf.py density --runtime=runc --runtime=runsc" log="true" y_min="100000" >}} 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 @@ -108,7 +107,7 @@ 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. -{{< graph id="sysbench-cpu" url="/performance/sysbench-cpu.csv" title="perf.py sysbench.cpu --runtime=runc --runtime=runsc" >}} +{{< graph id="sysbench-cpu" url="/performance/sysbench-cpu.csv" title="perf.py sysbench.cpu --runtime=runc --runtime=runsc" >}} 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 @@ -119,7 +118,7 @@ This has important consequences for classes of workloads that are often CPU-bound, such as data processing or machine learning. In these cases, `runsc` will similarly impose minimal runtime overhead. -{{< graph id="tensorflow" url="/performance/tensorflow.csv" title="perf.py tensorflow --runtime=runc --runtime=runsc" >}} +{{< graph id="tensorflow" url="/performance/tensorflow.csv" title="perf.py tensorflow --runtime=runc --runtime=runsc" >}} For example, the above figure shows a sample TensorFlow workload, the [convolutional neural network example][cnn]. The time indicated includes the @@ -133,7 +132,7 @@ 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. -{{< graph id="syscall" url="/performance/syscall.csv" title="perf.py syscall --runtime=runc --runtime=runsc-ptrace --runtime=runsc-kvm" y_min="100" log="true" >}} +{{< graph id="syscall" url="/performance/syscall.csv" title="perf.py syscall --runtime=runc --runtime=runsc-ptrace --runtime=runsc-kvm" y_min="100" log="true" >}} 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 @@ -144,7 +143,7 @@ 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. -{{< graph id="redis" url="/performance/redis.csv" title="perf.py redis --runtime=runc --runtime=runsc" >}} +{{< graph id="redis" url="/performance/redis.csv" title="perf.py redis --runtime=runc --runtime=runsc" >}} For example, `redis` is an application that performs relatively little work in userspace: in general it reads from a connected socket, reads or modifies some @@ -164,7 +163,7 @@ 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). -{{< graph id="startup" url="/performance/startup.csv" title="perf.py startup --runtime=runc --runtime=runsc" >}} +{{< graph id="startup" url="/performance/startup.csv" title="perf.py startup --runtime=runc --runtime=runsc" >}} The above figure indicates how total time required to start a container through [Docker][docker]. This benchmark uses three different applications. First, an @@ -176,7 +175,7 @@ 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 `runc` benchmark. To avoid these costs with `runsc`, > you may also consider using `runsc do` mode or invoking the [OCI -> runtime](../../user_guide/oci) directly. +> runtime](../../user_guide/oci/) directly. ## Network @@ -187,14 +186,14 @@ While typically not an important metric in practice for common sandbox use cases, nevertheless `iperf` is a common microbenchmark used to measure raw throughput. -{{< graph id="iperf" url="/performance/iperf.csv" title="perf.py iperf --runtime=runc --runtime=runsc" >}} +{{< graph id="iperf" url="/performance/iperf.csv" title="perf.py iperf --runtime=runc --runtime=runsc" >}} 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. -{{< graph id="applications" metric="requests_per_second" url="/performance/applications.csv" title="perf.py http.(node|ruby) --connections=25 --runtime=runc --runtime=runsc" >}} +{{< graph id="applications" metric="requests_per_second" url="/performance/applications.csv" title="perf.py http.(node|ruby) --connections=25 --runtime=runc --runtime=runsc" >}} The above figure shows the result of simple `node` and `ruby` web services that render a template upon receiving a request. Because these synthetic benchmarks @@ -215,20 +214,20 @@ through the [Gofer](../) as a result of our [security model](../security/), but in most cases are dominated by **implementation costs**, due to an internal [Virtual File System][vfs] (VFS) implementation that needs improvement. -{{< graph id="fio-bw" url="/performance/fio.csv" title="perf.py fio --engine=sync --runtime=runc --runtime=runsc" log="true" >}} +{{< graph id="fio-bw" url="/performance/fio.csv" title="perf.py fio --engine=sync --runtime=runc --runtime=runsc" log="true" >}} 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. -{{< graph id="fio-tmpfs-bw" url="/performance/fio-tmpfs.csv" title="perf.py fio --engine=sync --runtime=runc --tmpfs=True --runtime=runsc" log="true" >}} +{{< graph id="fio-tmpfs-bw" url="/performance/fio-tmpfs.csv" title="perf.py fio --engine=sync --runtime=runc --tmpfs=True --runtime=runsc" log="true" >}} 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. -{{< graph id="httpd100k" metric="transfer_rate" url="/performance/httpd100k.csv" title="perf.py http.httpd --connections=1 --connections=5 --connections=10 --connections=25 --runtime=runc --runtime=runsc" >}} +{{< graph id="httpd100k" metric="transfer_rate" url="/performance/httpd100k.csv" title="perf.py http.httpd --connections=1 --connections=5 --connections=10 --connections=25 --runtime=runc --runtime=runsc" >}} 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 @@ -241,16 +240,22 @@ internal serialization points (since all requests are reading the same file). Note that some of some of network stack performance issues also impact this benchmark. -{{< graph id="ffmpeg" url="/performance/ffmpeg.csv" title="perf.py media.ffmpeg --runtime=runc --runtime=runsc" >}} +{{< graph id="ffmpeg" url="/performance/ffmpeg.csv" title="perf.py media.ffmpeg --runtime=runc --runtime=runsc" >}} 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 for an `ffmpeg` container to start, load and transcode a 27MB input video. [ab]: https://en.wikipedia.org/wiki/ApacheBench + [benchmark-tools]: https://gvisor.googlesource.com/benchmark-tools + [gce]: https://cloud.google.com/compute/ + [cnn]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py + [docker]: https://docker.io + [redis-benchmark]: https://redis.io/topics/benchmarks + [vfs]: https://en.wikipedia.org/wiki/Virtual_file_system |