Linux Kernel Tuning: page allocation failure

If you start seeing these errors it means your server or instance started running out of kernel memory.

[10223.291166] java: page allocation failure: order:0, mode:0x1080020(GFP_ATOMIC), nodemask=(null)
[10223.301794] java cpuset=/ mems_allowed=0-1
[10223.307211] CPU: 29 PID: 19395 Comm: java Not tainted 4.14.154-99.181.amzn1.x86_64 #1
[10223.315658] Hardware name: Xen HVM domU, BIOS 08/24/2006
[10223.322004] Call Trace:
[10223.325230]  <IRQ>
[10223.328193]  dump_stack+0x66/0x82
[10223.332213]  warn_alloc+0xe0/0x180

In particular, these Order 0 (zero) errors, mean there isn’t even a single 4K page available to allocate.

This might sound weird on a system were we have a lot of RAM memory available. And actually, this may be a common situation on systems where the kernel is not tuned up properly.

In particular, we need to look at the following kernel parameter:


This is used to force the Linux VM to keep a minimum number
of kilobytes free.  The VM uses this number to compute a
watermark[WMARK_MIN] value for each lowmem zOn one in the system.
Each lowmem zone gets a number of reserved free pages based
proportionally on its size.

Some minimal amount of memory is needed to satisfy PF_MEMALLOC
allocations; if you set this to lower than 1024KB, your system will
become subtly broken, and prone to deadlock under high loads.

Setting this too high will OOM your machine instantly.

On systems with very large amount of RAM memory, this parameter is usually set too low. Change default value (have a look to the previous paragraph to avoid too low or too high values) and restart with sysctl. 1GB is the value I use on most of the large memory servers (64GB+).

sudo sed -i '${s/$/'"\nvm.min_free_kbytes = 1048576"'/}' /etc/sysctl.conf
sysctl vm.min_free_kbytes

echo "reloading the settings:"
sudo /sbin/sysctl -p

EBS Storage Performance Notes – Instance throughput vs Volume throughput

I just wanted to write a couple lines/guidance on this regard as this is a recurring question when configuring storage, not only in the cloud, but can also happen on bare metal servers.

What is throughput on a volume?

Throughput is the measure of the amount of data transferred from/to a storage device per time unit (typically seconds).

The throughput consumed on a volume is calculated using this formula:

IOPS (IO Ops per second) x BS (block size)= Throughput

As example, if we are writing at 1200 Ops/Sec, and the chunk write size is around 125Kb, we will have a total throughput of about 150Mb/sec.

Why is this important?

This is important because we have to be aware of the Maximum Total Throughput Capacity for a specific volume vs the Maximum Total Instance Throughput.

Because, if your instance type (or server) is able to produce a throughput of 1250MiB/s (i.e M4.16xl)) and your EBS Maximum Throughput is 500MiB/s (i.e. ST1), not only you will hit a bottleneck trying to write to the specific volumes, but also throttling might occur (i.e. EBS on cloud services).

How do I find what is the Maximum throughput for EC2 instances and EBS volumes?

Here is documentation about Maximum Instance Throughput for every instance type on EC2:

And here about the EBS Maximum Volume throughput:

How do I solve the problem ?

If we have an instance/server that has more throughput capabilities than the volume, just add or split the storage capacity into more volumes. So the load/throughput will be distributed across the volumes.

As an example, here are some metrics with different volume configurations:

1 x 3000GB – 9000IOPS volume:


3 x 1000GB – 3000IOPS volume:


Look at some of the metrics: these are using the same instance type (m4.10xl – 500Mb/s throughput), same volume type (GP2 – 160Mb/s throughput) and running the same job:

  • Using 1 volume, Write/Read Latency is around 20-25 ms/op. This value is high compared to 3x1000GB volumes.
  • Using 1 volume, Avg Queue length 25. The queue depth is the number of pending I/O requests from your application to your volume. For maximum consistency, a Provisioned IOPS volume must maintain an average queue depth (rounded to the nearest whole number) of one for every 500 provisioned IOPS in a minute. On this scenario 9000/500=18. Queue length of 18 or higher will be needed to reach 9000 IOPS.
  • Burst Balance is 100%, which is Ok, but if this balance drops to zero (it will happen if volume capacity keeps being exceeded), all the requests will be throttled and you’ll start seeing IO errors.
  • On both scenarios, Avg Write Size is pretty large (around 125KiB/op) which will typically cause the volume to hit the throughput limit before hitting the IOPS limit.
  • Using 1 volume, Write throughput is around 1200 Ops/Sec. Having write size around 125Kb, it will consume about 150Mb/sec. (IOPS x BS = Throughput)