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Google 推出 gVisor 強化 Container 的安全性

Google 發表了 gVisor,針對 Linux 所使用的 container 技術強化安全的部份:「Open-sourcing gVisor, a sandboxed container runtime」。

依照 Google 的說法,一般 container 的架構是這樣:

而具有強隔離性的 VM 技術則是這樣:

在 VM 的 overhead 偏重,但一般的 container 安全性又不夠。而 gVisor 則是這樣:

對於目前最常見的 Docker 系統上,在安裝 gVisor 後只需要指定 --runtime=runsc 就可以使用 (預設是 --runtime=runc),像是這樣:

$ docker run --runtime=runsc hello-world
$ docker run --runtime=runsc -p 3306:3306 mysql

其中 runsc 的意思是「run Sandboxed Container」。

另外而因為 gVisor 卡在中間,不認識的 syscall 都會被擋下來,所以目前並不是所有的應用程式都可以跑,但開發團隊已經測了不少應用程式可以在上面運作,算是堪用的程度:

gVisor implements a large part of the Linux system API (200 system calls and counting), but not all. Some system calls and arguments are not currently supported, as are some parts of the /proc and /sys filesystems. As a result, not all applications will run inside gVisor, but many will run just fine, including Node.js, Java 8, MySQL, Jenkins, Apache, Redis, MongoDB, and many more.

值得一提的是,雖然是處理 syscall,但是是用 Go 開發的,而不是 C 或是 C++,這點頗特殊的...

不同性質的應用程式對 KPTI (Meltdown 修正) 的效能影響

NetflixBrendan Gregg 整理了他測試 KPTI 對效能的影響:「KPTI/KAISER Meltdown Initial Performance Regressions」。

與其他人只是概括的測試,他主要是想要針對可量測的數字對應出可能的 overhead,這樣一來還沒上 patch 的人就可以利用這些量測數字猜測可能的效能衝擊。

他把結論放在前面:

To understand the KPTI overhead, there are at least five factors at play. In summary:

  • Syscall rate: there are overheads relative to the syscall rate, although high rates are needed for this to be noticable. At 50k syscalls/sec per CPU the overhead may be 2%, and climbs as the syscall rate increases. At my employer (Netflix), high rates are unusual in cloud, with some exceptions (databases).
  • Context switches: these add overheads similar to the syscall rate, and I think the context switch rate can simply be added to the syscall rate for the following estimations.
  • Page fault rate: adds a little more overhead as well, for high rates.
  • Working set size (hot data): more than 10 Mbytes will cost additional overhead due to TLB flushing. This can turn a 1% overhead (syscall cycles alone) into a 7% overhead. This overhead can be reduced by A) pcid, available in Linux 4.14, and B) Huge pages.
  • Cache access pattern: the overheads are exacerbated by certain access patterns that switch from caching well to caching a little less well. Worst case, this can add an additional 10% overhead, taking (say) the 7% overhead to 17%.

重點在於給了量測的方式,以第一個 Syscall rate 來說好了,他用 sudo perf stat -e raw_syscalls:sys_enter -a -I 1000 測試而得到程式的 syscall 數量,然後得到下面的表格,其中 X 軸是每秒千次呼叫數,Y 軸是效能損失:

用這樣的方式提供給整個組織 (i.e. Netflix) 內評估衝擊。

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