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Assume I have a pod with 3 containers: X, Y, and Z.
K8S can set a cpu limit for each container in a pod. However, if I set 1000M CPU limit to each container, then any container cannot use more than 1000M CPU even if the other two are ilde, which is not what I want.
I want to set a CPU quota of 3000M to the pod, rather than to each container. For example, if X & Y are idle, Z can use 3000M CPU; if X is using 1500M CPU, Y is using 1000M CPU, then Z can only use 500M CPU.
So, my question is:
How to share a CPU quota among multiple containers?
I must set limits because I must pay for my usage to the cloud provider.
In such a case, I would recommend you to use Vertical-Pod-AutoScaler along with a Limit Range.
A LimitRange provides constraints that can:
Enforce minimum and maximum compute resources usage per Pod or Container in a namespace.
And the VPA will try to cap recommendations between min and max of limitRanges based on the current usages.
N.B.: Make sure you have metrics-server installed in your cluster to enable the VPA.
Just don't set a CPU limit at all. Or set it to some higher value. Setting a CPU limit should only happen in situations where it's known to be needed, in general they do more harm than good.
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Our system is consuming swap memory even system has avaiable memory. Is that behaviour normal ?
Our system is redhat 8.6.
memory usage figure
Solution for memory usage problem.
The Linux 2.6 kernel introduced a new kernel parameter called swappiness, which allows administrators to customize how Linux swaps.
Swappiness is a property for the Linux kernel that changes the balance between swapping out runtime memory, as opposed to dropping pages from the system page cache. Swappiness can be set to values between 0 and 100, inclusive. A low value means the kernel will try to avoid swapping as much as possible where a higher value instead will make the kernel aggressively try to use swap space.
Since the linux kernel 5.8 has a swappiness value range of 0 to 200 and a default value of 60. You can change it temporarily (until your next reboot) with the following command
echo 42 > /proc/sys/vm/swappiness
If you want to change it permanently, edit the vm.swappiness parameter in the /etc/sysctl.conf file.
It should be noted that the swappiness number does not imply that 60% of memory will be moved into swap. There is a swap algorithm that determines when and how much data is put into swap.
The following formula is provided by Redhat to determine swap tendency:
swap_tendency = mapped_ratio/2 + distress + vm_swappiness;
You can read more here
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I am running a test on Ubuntu 14.04. When I check my CPU usage using
'ps aux|grep service' then CPU usage is 0.1 of a process, while in htop for the same process the CPU% is 12.3.
Can anyone tell me the reason? or which value should I consider the right one?
Thanks
They are measuring different things.
From the ps man-page:
CPU usage is currently expressed as the percentage of time spent
running during the entire lifetime of a process. This is not ideal,
and it does not conform to the standards that ps otherwise conforms to.
CPU usage is unlikely to add up to exactly 100%.
From the htop man-page (I am the author of htop):
PERCENT_CPU (CPU%)
The percentage of the CPU time that the process is currently
using.
So, in htop this is the percentage of total CPU time used by the program between the last refresh of the screen and now.
PercentageInHtop = (non-idle CPU time used by process during the last 1.5s) / 1.5s
In ps this is the percentage of CPU time used by the program relative to the total time it exists (ie, since it was launched).
PercentageInPs = (non-idle CPU time used by process since process startup) / (time elapsed since process startup)
That is, in your reading it means that htop is saying that the service is taking 12.3% of your CPU now, while ps is saying that your service has spent 99.9% of its total life idle.
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My current configs are:
> cat /proc/sys/vm/panic_on_oom
0
> cat /proc/sys/vm/oom_kill_allocating_task
0
> cat /proc/sys/vm/overcommit_memory
1
but when I run a task, it's killed anyway.
> ./test/mem.sh
Killed
> dmesg | tail -2
[24281.788131] Memory cgroup out of memory: Kill process 10565 (bash) score 1001 or sacrifice child
[24281.788133] Killed process 10565 (bash) total-vm:12601088kB, anon-rss:5242544kB, file-rss:64kB
Update
My tasks are used to scientific computing, which costs many memories, it seems that overcommit_memory=1 may be the best choice.
Update 2
Actually, I'm working on a data analyzation project, which costs memory more than 16G, but I was asked to limit them in about 5G. It might be impossible to implement this requirement via optimizing the program itself, because the project uses many sub-commands, and most of them does not contains options like Xms or Xmx in Java.
Update 3
My project should be an overcommited system. Exacetly as what a3f saying, it seems that my apps prefer to crash by xmalloc when mem allocated failed.
> cat /proc/sys/vm/overcommit_memory
2
> ./test/mem.sh
./test/mem.sh: xmalloc: .././subst.c:3542: cannot allocate 1073741825 bytes (4295237632 bytes allocated)
I don't want to surrender, although so many aweful tests make me exhausted.
So please show me a way to the light ; )
The OOM killer won't go away. If there is no memory, someone's got to pay. What you can do is set a limit after which memory allocations fail.
That's exactly what setting vm.overcommit_memory to 2 achieves.
From the docs:
The Linux kernel supports the following overcommit handling modes
2 - Don't overcommit. The total address space commit for the system
is not permitted to exceed swap + a configurable amount (default is
50%) of physical RAM. Depending on the amount you use, in most
situations this means a process will not be killed while accessing
pages but will receive errors on memory allocation as appropriate.
Normally, the kernel will happily hand out virtual memory (overcommit). Only when you reference a page, the kernel has to map the page to a real physical frame. If it can't service that request, a process needs to be killed by the OOM killer to make space.
Disabling overcommit means that e.g. malloc(3) will return NULL if the kernel couldn't commit the amount of memory requested. This makes things a bit more predictable, albeit limited (many applications allocate more than they would ever need).
The possible values of oom_adj range from -17 to +15. The higher the score, more likely the associated process is to be killed by OOM-killer. If oom_adj is set to -17, the process is not considered for OOM-killing.
But, increase ram is better choice ,if increasing ram is not possible, then add swap memory.
To increase swap memory try this link,
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I found at several places that Linux uses pages and a paging mechanism but I didn't find anywhere where this file is or how to configure it.
All the information I found is about the Linux swap file / partition. There is a difference between paging and swapping:
Paging moves pages (a small frame which contains a piece of data - usually 4 KB but can vary between different OS's) from main memory to a backbend storage, happens always as a normal function of the operating system.
Swapping moves an entire process to storage and happens when the system is memory stressed or on windows 8 when a new application is hibernating.
Does Linux uses it's swap file / partition for both cases?
If so, how could I see how many page are currently paged out? This information is not there in vmstat, free or swapon commands (or that I fail to see it).
Or is there another file used for paging?
If so, how can I configure it (and watch it's usage)?
Or perhaps Linux does not use paging at all and I was mislead?
I would appreciate if the answers will be specific to red hat enterprise Linux both versions 6 and 7 but also a general answer about all Linux's will be good.
Thanks in advance.
On Linux, the swap partition(s) are used for paging.
Linux does not respond to memory pressure by swapping out whole processes. The virtual memory system does demand paging, page by page. Under extreme memory pressure, one or more processes will be killed by the OOM killer. (There are some useful links to documentation in the first NOTE in man malloc)
There is a line in the top header which shows swap partition usage, but if that is all the information you want, use
swapon -s
man swapon for more information.
The swap partition usage is not the same as the number of unmapped pages. A page might be memory-mapped to a file using the mmap call; since that page has backing store in the file, there is no need to also write it to a swap partition, and the system won't use swap space for that. But swap partition usage is a pretty good indicator.
Also note that Linux (unlike Windows) does not allocate swap space for pages when they are allocated. Instead, it adds the new page to the virtual memory map without any backing store. and allocates the swap space when the page needs to be swapped out. The consequence (as described in the malloc manpage referenced earlier) is that a malloc call may succeed in allocating virtual memory, but a subsequent attempt to use that virtual memory may fail.
Although Linux retains the term 'swap partition' as a historical relic, it actually performs paging. So your expectation is borne out; you were just thrown by the archaic terminology.
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I have 6 devices: 4TB, 3TB, 2TB, 2TB, 1.5TB, 1TB (/dev/sda to /dev/sdf).
First question:
With RAID-1 I'd have:
2TB mirrored in 2TB
1TB mirrored in 0.5#4TB + 0.5#3TB
1.5TB mirrored in 1.25#4TB + 0.25#3TB
the rest 2.25 of 3TB mirrored in the rest 2.25TB of 4TB.
My total size would be in that case (4 + 3 + 2 + 2 + 1.5 + 1) = 13.5/2 = 6.75TB
Will $ mkfs.btrfs --data raid1 --metadata raid1 /dev/sda /dev/sdb /dev/sdc /dev/sdd /dev/sde /dev/sdf provide me with approximately 6.75TB? If yes, how many disks (and which?) can I afford losing?
Second question:
With the RAID-1 I can afford, for example, losing three disks:
one 2TB disk,
the 1TB disk and
the 1.5TB disk,
without losing data.
How can I have the same freedom in losing the same disks with btrfs?
Thanks!
Btrfs distributes the data (and its RAID 1 copies) block-wise, thus deals very well with hard disks of different size. You will receive the sum of all hard disks, divided by two – and do not need to think on how to put them together in similar sized pairs.
If more than one disk fails, you're always in danger of losing data: RAID 1 cannot deal with losing two disks at the same time. In your example given above, if the wrong two disks die, you always lose data.
Btrfs can increase the chances of losing data if more than one disk fails, as it will distribute the blocks somewhat randomly, chances are higher that some blocks are only stored on the failed two devices. On the other hand, if you lose data, you probably lose less for the same reason. In average, it sums up to the same chance of losing n bits, but if you're interested in the chance of losing only a single bit you're worse of with btrfs.
And then again, you should also consider its advantage of using checksums which help against corrupted data on disk.