You may need to use one of the latest versions of the kernel and patch it with the appropriate BFS patch. Go and fetch the latest kernel (I choose version 2.6.31) from kernel.org:
Figure 1: xkcd.com comic
picks on Linux cd /usr/srcwget -c http://www.kernel.org/pub/linux/kernel/v2.6/linux-2.6.31.tar.bz2
Then download the BFS patch for that kernel and apply the patch after extracting the kernel source:
wget -c http://ck.kolivas.org/patches/bfs/2.6.31-sched-bfs-220.patch tar xjvf linux-2.6.31.tar.bz2
cd linux-2.6.31
patch -p1 < ../2.6.31-sched-bfs-220.patch
Then compile the kernel. If you are using Ubuntu, you can use the make-kpkg command to build the deb packages for kernel-image and kernel-headers. You may need to install kernel-package prior to this. It is as simple as typing a sudo apt-get install kernel-package. You can copy the current kernel configuration as follows: cp -vi /boot/config-`uanme -r` .config
Note that since Ubuntu kernels are built with Debugging info enabled, it makes the kernel modules much larger in size than they should be. So, if you're using the existing configuration, it's advisable to turn off the Debugging info mode. While you're at make menuconfig, go to ‘Kernel hacking’, and under ‘Kernel debugging’ turn off ‘Compile the kernel with debug info’.
If you want to speed up the compilation process, you can enable parallel make by setting the CONCURRENCY_ LEVEL environment variable. Set it to 1 plus the number of processor cores you have. For example, if you have a dual- core processor, use:
export CONCURRENCY_LEVEL=3 Change the kernel configuration: make menuconfig
Additionally, if you're not compiling for the first time, run the following command for a clean build:
make-kpkg clean Finally:
fakeroot make-kpkg --initrd --append-to-version=-bfskernel kernel-image kernel-headers
This will take a long time and after this step, you will get two files, namely:
linux-headers-2.6.31-bfs211-bfskernel_2.6.31-bfs211- bfskernel-10.00.Custom_i386.deb
linux-image-2.6.31-bfs211-bfskernel_2.6.31-bfs211- bfskernel-10.00.Custom_i386.deb.
Install them using the dpkg -i <deb file> command.
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make -j2 performance while compiling Python 2.6.2
when using BFS when using CFS
real 1 minute, 34.297
seconds 1 minute 41.841 seconds user 1 minute, 56.151
seconds 1 minutes 57.387 seconds sys 7.836 seconds 8.309 seconds
Table 1
make performance while compiling Python 2.6.2
when using BFS when using CFS
real 2 minutes, 2.595
seconds 2 minutes, 4.598 seconds user 1 minute, 54.183
seconds 1 minute, 54.519 seconds sys 7.576 seconds 7.836 seconds
Table 2 Some users from the Ubuntu Forums reported that they have experienced a speed difference in GDM. “Before the patch, there was a period of some five seconds during which GDM seemed to be in deep thought mode, trying to decide which users to show in this list. After the patch the list appeared almost immediately.”
There have been only positive reviews of the BFS scheduler throughout the Linux community, as far as I could see. As the developer says, BFS is not stable yet and when it reaches a stable release, I'm pretty sure it will have something more to offer. By the way, Kolivas concludes his FAQ page by pointing out, “Yes I know you probably still can't watch full screen videos on YouTube, but that's not entirely the scheduler's fault.”
Then reboot your machine to the new kernel. You will get a new kernel entry in Grub that looks like "Ubuntu 9.04, kernel 2.6.31-bfs211-bfskernel". Log in and experience the difference.
Performance analysis
As Kolivas explains in his FAQ page, it is not the number of physical CPUs that affect BFS's scalability. Rather, it's that of logical ones. This means that a hyper-threaded quad core CPU is simulated as 8 logical CPUs.
People are also concerned about the O(n) lookup of BFS. However, the actual overhead is very small. Even with large numbers of n, it has lower overhead than an O(1) design. During a test against CFS, in which the test application forks 1,000 simple tasks, it showed no difference in time as compared to CFS on a quad core machine. But BFS will be much faster when the load is lower and the CPUs are less in number, which is normally the case with desktops.
Kolivas cites an example in the FAQ page, “See the reverse scalability graph, courtesy of Serge Belyshev, showing various job numbers on a kernel build on a quad core machine [Figure 2]. The problem has always been that the mainline scheduler can't keep the CPUs busy enough; i.e., it doesn't make the most of your hardware in the most common situations on a desktop! Note that the reverse scalability graph is old; the scalability has improved since then.”
To test the speed of the new scheduler, I compiled Python 2.6.2 with a make -j2 on my laptop that has a Core2Duo processor and 2GB of RAM. It took only 1 minute and 34 seconds to complete on the new BFS patched kernel. The compilation using simple make (without the -jX option) completed in 2 minutes and 2 seconds.
Earlier, I had done this test on the stock kernel that uses CFS. But it took 1 minute and 41 seconds using make -j2 and 2 minutes and 4 seconds without using the -jX option to compile the same version of Python. So the speed difference is obvious. Refer to Tables 1 and 2 for the time differences.
References
Wikipedia on schedulers: en.wikipedia.org/wiki/Scheduling_ (computing)
Types of scheduling: http://www.cim.mcgill.ca/~franco/ OpSys-304-427/lecture-notes/node38.html
Wikipedia on CFS: en.wikipedia.org/wiki/Completely_Fair_ Scheduler
BFS FAQ: ck.kolivas.org/patches/bfs/bfs-faq.txt Con Kolivas interview: ck.kolivas.org/german_linux_ magazine_interview.txt
The reverse scalability graph is taken from ck.kolivas.org/ patches/bfs/reverse-scalability.png • • • • • • By: Sreejith K.
The author is a FOSS enthusiast and a hardcore Ubuntu fan who loves hacking GNU/Linux. He's currently working as a development engineer at K7 Computing, Chennai. His areas of interest include compilers, virtualisation technologies and cloud computing. He blogs at http://semk.in and can be reached at sreejithemk AT gmail DOT com.
Figure 2: The reverse scalability graph shows various job numbers on a kernel build on a quad core machine
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V
irtual Private Networks, or VPN for short, allow us to establish a private network over a public network (like the Internet) using cryptography to secure it. An SSL VPN uses an SSL (Secure Sockets Layer), enabling the service to be accessible over the HTTPS protocol using a Web browser. OpenVPN is an example of an SSL VPN solution that's open source and is targeted at SMEs and enterprises.A wide range of configurations exist for OpenVPN, including site-to-site VPNs,
remote access, Wi-Fi security, etc. It uses the TUN interface for virtual linking of the two end points of the VPN. The TUN interface looks like a point-to-point network hardware device to the operating system, but is, in fact, nothing but a virtual network adopter. Thus being a virtual interface, the data is actually pushed to the user-space program, which can then open a TUN device (like a file), and read and write IP packets from and to it.
Note that a TAP interface is similar to TUN except that it emulates the Ethernet