| commit | a34c08c8c5a505e55178a9a8ef5391224d5460a5 | [log] [tgz] |
|---|---|---|
| author | Andrew Yourtchenko <ayourtch@gmail.com> | Thu May 24 16:53:27 2018 +0200 |
| committer | Damjan Marion <dmarion.lists@gmail.com> | Sat May 26 16:56:02 2018 +0000 |
| tree | 961461e2a4261dcea81b21e2eddfb026c3d01b8e | |
| parent | c6f186b23d00685b3e9f132ba79a5cb44f0a44c0 [diff] |
acl-plugin: create forward and return sessions in lieu of making a special per-packet session key Using a separate session key has proven to be tricky for the following reasons: - it's a lot of storage to have what looks to be nearly identical to 5tuple, just maybe with some fields swapped - shuffling the fields from 5tuple adds to memory pressure - the fact that the fields do not coincide with the packet memory means for any staged processing we need to use up a lot of memory Thus, just add two entries into the bihash table pointing to the same session entry, so we could match the packets from either direction. With this we have the key layout of L3 info (which takes up the majority of space for IPv6 case) the same as in the packet, thus, opening up the possibility for other optimizations. Not having to create and store a separate session key should also give us a small performance win in itself. Also, add the routine to show the session bihash in a better way than a bunch of numbers. Alas, the memory usage in the bihash obviously doubles. Change-Id: I8fd2ed4714ad7fc447c4fa224d209bc0b736b371 Signed-off-by: Andrew Yourtchenko <ayourtch@gmail.com>
The VPP platform is an extensible framework that provides out-of-the-box production quality switch/router functionality. It is the open source version of Cisco's Vector Packet Processing (VPP) technology: a high performance, packet-processing stack that can run on commodity CPUs.
The benefits of this implementation of VPP are its high performance, proven technology, its modularity and flexibility, and rich feature set.
For more information on VPP and its features please visit the FD.io website and What is VPP? pages.
Details of the changes leading up to this version of VPP can be found under @ref release_notes.
| Directory name | Description |
|---|---|
| build-data | Build metadata |
| build-root | Build output directory |
| doxygen | Documentation generator configuration |
| dpdk | DPDK patches and build infrastructure |
| @ref extras/libmemif | Client library for memif |
| @ref src/examples | VPP example code |
| @ref src/plugins | VPP bundled plugins directory |
| @ref src/svm | Shared virtual memory allocation library |
| src/tests | Standalone tests (not part of test harness) |
| src/vat | VPP API test program |
| @ref src/vlib | VPP application library |
| @ref src/vlibapi | VPP API library |
| @ref src/vlibmemory | VPP Memory management |
| @ref src/vlibsocket | VPP Socket I/O |
| @ref src/vnet | VPP networking |
| @ref src/vpp | VPP application |
| @ref src/vpp-api | VPP application API bindings |
| @ref src/vppinfra | VPP core library |
| @ref src/vpp/api | Not-yet-relocated API bindings |
| test | Unit tests and Python test harness |
In general anyone interested in building, developing or running VPP should consult the VPP wiki for more complete documentation.
In particular, readers are recommended to take a look at [Pulling, Building, Running, Hacking, Pushing](https://wiki.fd.io/view/VPP/Pulling,_Building,_Run ning,_Hacking_and_Pushing_VPP_Code) which provides extensive step-by-step coverage of the topic.
For the impatient, some salient information is distilled below.
To install system dependencies, build VPP and then install it, simply run the build script. This should be performed a non-privileged user with sudo access from the project base directory:
./extras/vagrant/build.sh
If you want a more fine-grained approach because you intend to do some development work, the Makefile in the root directory of the source tree provides several convenience shortcuts as make targets that may be of interest. To see the available targets run:
make
The directory extras/vagrant contains a VagrantFile and supporting scripts to bootstrap a working VPP inside a Vagrant-managed Virtual Machine. This VM can then be used to test concepts with VPP or as a development platform to extend VPP. Some obvious caveats apply when using a VM for VPP since its performance will never match that of bare metal; if your work is timing or performance sensitive, consider using bare metal in addition or instead of the VM.
For this to work you will need a working installation of Vagrant. Instructions for this can be found [on the Setting up Vagrant wiki page] (https://wiki.fd.io/view/DEV/Setting_Up_Vagrant).
Several modules provide documentation, see @subpage user_doc for more end-user-oriented information. Also see @subpage dev_doc for developer notes.
Visit the VPP wiki for details on more advanced building strategies and other development notes.
There is PyDoc generated documentation available for the VPP test framework. See @ref test_framework_doc for details.