Patch the Linux Kernel Without Reboots
evanbro writes "ZDNet is reporting on ksplice, a system for applying patches to the Linux kernel without rebooting. ksplice requires no kernel modifications, just the source, the config files, and a patch. Author Jeff Arnold discusses the system in a technical overview paper (PDF). Ted Ts'o comments, 'Users in the carrier grade linux space have been clamoring for this for a while. If you are a carrier in telephony and don't want downtime, this stuff is pure gold.'"
Update: 04/24 10:04 GMT by KD : Tomasz Chmielewsk writes on LKML that the idea seems to be patented by Microsoft.
If you are a carrier in telephony, you should have many load-balanced servers that can be taken offline one at a time and restored after patching. They probably would be taken out of the loop for the in-place patching anyway. So who is "clamoring"?
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That is truly amazing tech, right there. It would be interesting to know the security implications of being able to hot-patch the kernel, however.
Trying to keep one server up 24/7/365 is a usually mistake. You'll never achieve 100% uptime. A much better idea is to use clustering and distributed computing so your overall system can survive the loss of individual servers.
The key sequence to access my Slashdot bookmark in Firefox is Alt-B-S. I don't believe this is a coincidence.
There was a kernel exploit recently where someone submitted a patch that modified the running kernel using this technology. It didn't work for me, so I had to resort to patching the .c that was affected - but a lot of people reported that it worked.
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I thought their working slogan was:
Windows 7, it's not awful like Vista!
Can ksplice be installed without rebooting?
He basically compiles a patched and unpatched kernel with the same compiler, compares the ELF output, and uses that to generate a binary file that corresponds to the change. That gets wrapped in a generic module for use, another module installs it along with JMPs to bypass the old code and use the new, and he performs the checks needed to make sure he can safely install the redirects.
He also has to differentiate real changes from incidental ones (the example given is changing the address of a function - all references to it will change, but they don't really need to be included in the binary diff).
The only human work required is to check whether a patch makes semantic changes to a data structure... whether eg. an unsigned integer variable that was being used as a number is now a packed set of flags - the data declaration is the same, but it's being used differently.
Interesting paper. Also a useful new set of capabilities for any Linux user who can't handle downtime for quarterly patching... worth its weight in gold in some businesses.
Erik
I'd rather have at least two of anything important and have statefull failover between them.
If you've got this system that's so critical you can't reboot it for a kernel upgrade, what do you do when the building catches fire or a tanker truck full of toxic waste hops the curb and plows through the wall of your datacenter?
I'd rather have a full second set of anything that critical. It should be in a different state (or country) and have a well designed and frequently used method of seamlessly transferring the load between the two (or more) sites without dropping anything.
If you can't transfer the workload to a location at least a couple hundred miles away without users noticing then you're not in the big league.
And as long as the workload is in another datacenter, what's the big deal about rebooting for a kernel upgrade.
Not only the CEO. I lived to see even a hardline IT guy (admittedly, one whose goal in life seems to be to be against whatever you want, and to avoid doing any extra work... actually, make that just: any work) argue along the lines of "nooo, you can't have the servers only 60% loaded! It's a waste of valuable hardware! Why, back in my day (of batch jobs on punched cards, presumably) we had the mainframe used at least an average of 95% before asking for an extra server!"
It always irks me to see people just not understand concepts like "peak" vs "average", or "failing over".
- A cluster of, say, 4 machines (small application, really) which are loaded to 90% of capacity, if one dies, the other 3 are now at 120% of capacity each. If you're lucky, it just crawls, if you're unlucky, Java clutches its chest and keels over with an "OutOfMemoryError" or such.
- if you're at 90% most of the time, then fear Monday 9:00 AM, when every single business partner on that B2B application comes to work and opens his browser. Or fear the massive year-end batch jobs, when that machine/cluster sized barely enough to be ready with the normal midnight jobs by 9 AM, so those users can see their new offers and orders in their browsers, now has to do 20 times as much in a burst.
Basically it amazes me how many people just don't seem to get that simple rule of thumb of clusters: you're either getting nearly 100% uptime and nearly guaranteed response times, _or_ you're getting that extra hardware fully used to support a bigger load. Not both. Or not until that cluster is so large that 1-2 servers failing add negligible load to the remaining machines.
A polar bear is a cartesian bear after a coordinate transform.
A company that I once had dealings with was quite proud of their five nines. The motivation? It cost them $18,000 per second they were down. 30 seconds isn't just 30 seconds sometimes.
As an admin for some -very- high availability systems, load balancers are not a silver bullet. This solution would most apply for running one-node clusters who are using a single machine as a perimeter network device. (ex. firewall) I see lots of these in the racks at our NOC provider.
1. We connect to several load balanced systems and the complexity introduced by load balancers translates to inexplicable down time. No load balancers means a pretty steady diet of the latest and greatest server hardware, but no down time. The a few minutes of down time costs more than the server hardware.
2. High availability translates more roughly into nodes that can fail (ex. power off) and not take the cluster down. This boils down to active-passive application architecture more than just using heartbeat.
As an FYI, PostgreSQL clustering is a killer application for me. Erlang is also great in many ways, but requires application architecture with active-passive node awareness. Which isn't present in things like Yaws, or even my other favorite non-erlang app nginx. Heartbeat is the solution there, but I'd like to see yaws be cluster aware on its own. http://yaws.hyber.org/
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