After publishing the article “impact of memory reservations” I received a lot of questions about setting memory reservation at resource pool level. It seems there are several common facts about resource pools and memory reservations that are often misunderstood.
Because reservations are used by the VMkernel\DRS resource schedulers and (HA) admission control, the behavior of reservation can be very confusing. Before memory reservation on resource pool is addressed, let look at which mechanisms uses reservations and when reservations are used.
When are reservations actually used besides admission control?
If a cluster is under-committed the VM resource entitlement will be the same as its demand, in other words, the VM will be allocated whatever it wants to consume within its configured limit.
When a cluster is overcommitted, the cluster experiences more resource demand than its current capacity, at this point DRS and the VMkernel will allocate resources based on the resource entitlement of the virtual machine. Resource entitlement is covered later in the article.
Is there any difference between resource pool level and virtual machine level memory reservation?
To keep it short, VM level reservation can be rather evil, it will hoard memory if it has been used by the virtual machine once. Even if the virtual machine becomes idle, the VMkernel will not reclaim this memory and return it to the free memory set. This means that ESX can start swapping and ballooning if no free memory is available for other virtual machines while the owning VM’s aren’t using their claimed reserved memory. It also has influence on the slot size of High availability, for more information about HA slot sizes, please visit the HA deep dive page at yellow-bricks.com. For more information about virtual machine level memory reservation, please read the article “impact of memory management“.
Behavior of resource pool memory reservation
Now setting a memory reservation on a resource pool level has its own weaknesses, but it is much fairer and more along the whole idea of consolidation and sharing than virtual machine memory reservations. RP level reservations are immediately active, but are not claimed. This means it will only subtract the specified amount of memory from the unreserved capacity of the cluster.
RP reservations are used when children of the resource pool uses memory and the system is under contention. Reservations are not wasted and the resources can be used by other virtual machines. Be aware, using and reserving are two distinct concepts! Virtual machines can use the resource, but they cannot reserve this as well if it is already reserved by another item.
It appears that resource pool memory reservations work almost similar to CPU reservations, they won’t let any resource go to waste. And to top it off, resource pool reservations don’t flow to virtual machines, they will not influence HA slot sizes. Which unfortunately can lead to (temporary) performance loss if a host failover occurs. When a virtual machine is restarted by HA they are not restarted in the correct resource pool but in the root resource pool, which can lead to starvation. Until DRS is invoked, the virtual machine need to do it without any memory reservations.
How to use resource pool memory reservation?
Ok so two popular strategies exist when it comes to setting memory reservation on resource pool levels:
1. CPU and Memory reservations within the resource pool is never overcommitted i.e configured memory all VM’s (40Gb) equals reservation (40GB)
2. Percentage of Cluster resources reserved i.e. memory reservation resource pool (20GB) less than configured memory virtual machines inside RP (40GB)
The process of divvying is rather straightforward if the memory reservation equals the configured memory of the virtual machines inside the resource pool. All pages by the virtual machines are backed by machine pages, the resource entitlement is at least as large as its memory reservation.
What I find more interesting is what happens if the resource pool is configured with a memory reservation that is less than all virtual machine configured memory? DRS will divvy memory reservations based on the virtual machine resource entitlement.
So how is resource entitlement calculated?
A virtual machines resource entitlement is based on various statistics and some estimation techniques. DRS computes a resource entitlement for each virtual machine, based on virtual machine and resource pool configured shares, reservations, and limits settings, as well as the current demands of the virtual machines and resource pools, the memory size, its working set and the degree of current resource contention.
Now by setting a reservation on the resource pool level, the virtual machines who are actively using memory profits the most of this mechanism. Basically if no reservation is set on the VM level, the “RP” reservation is granted to all virtual machines inside the resource pool who are actively using memory.
DRS and the VMkernel calculates the resource pool and the virtual machine share levels. Please read the article “the resource pool priority-pie paradox” to get more information about share levels. and use this to specify the virtual machines priority.
Besides the share level the active utilization (working set) and the configured memory size are both accounted when calculating the resource entitlement. Virtual machines who are idling aren’t competing for resources, so they won’t get any new resources. If the memory is also idle the allocation get adjusted by the idle memory tax. Idle memory tax uses a progressive tax rate, the more idle memory a VM has, the more tax it will generate, this is why the configured memory size is also taken into account. (nice ammo if your customer wants to configure the DHCP server with 64GB memory!)
When we create a “Diva” VM (coined by Craig Risinger), that is setting VM level reservations, this allocation setting is passed to the VMkernel. It will subtract the specified amount of the reservation pool of the RP and it will not share it with others, i.e. the Diva VM is a special creature.
As stated above, RP memory reservations flow more than VM-level reservation, it will not claim\hoard memory. So basically when setting a resource pool reservation, reservations are just a part of the computation of the virtual machines resource entitlement. When the host is overcommitted, the memory usage of the virtual machine is either above or below the resource entitlement. If the memory usage exceeds its resource entitlement, the memory is ballooned or swapped from the virtual machine until it is at or below its entitlement.
Disclosure
Now before you think I fabricated this article all by myself I am happily to admit that I’m in the lucky position to work for VMware and to call some of the world brightest minds my colleagues. Kit Colbert, Carl Waldspurger and Chirag Bhatt took the time and explain this theory very thoroughly to me. Luckily
my colleagues and good friends Duncan Epping and Craig Risinger helped me decipher some out-of-this-world emails from the crew above and participated in some excellent discussions.
DRS Resource Distribution Chart
A customer of mine wanted more information about the new DRS Resource Distribution Chart in vCenter 4.0, so I thought after writing the text for the customer, why not share this? The DRS Resource Distribution Chart was overhauled in vCenter 4.0 and is quite an improvement over the resource distribution chart featured in vCenter 2.5. Not only does it use a better format, the new charts produce more in-depth information.
[Read more…] about DRS Resource Distribution Chart
Resource pools and avoiding HA slot sizing
Virtual machines configured with large amounts of memory (16GB+) are not uncommon these days. Most of the time these “heavy hitters” run mission critical applications so it’s not unusual setting memory reservations to guarantee the availability of memory resources. If such a virtual machine is placed in a HA cluster, these significant memory reservations can lead to a very conservative consolidation ratio, due to the impact on HA slot size calculation. (For more information about slot size calculation, please review the HA deep dive page on yellow-bricks.com.)
There are options to avoid creation of large slot sizes. Such as not setting reservations, disabling strict admission control, using vSphere new admission control policy “percentage of cluster resources reserved” or creating a custom slot size by altering the advanced settings das.vmMemoryMinMB.
But what if you are still using ESX 3.5, must guarantee memory resources for that specific VM, do not want to disable strict admission control or don’t like tinkering with the custom slot size setting? Maybe using the resource pool workaround can be an option.
Resource pool workaround
During a conversation with my colleague Craig Risinger, author of the very interesting article “The resource pool priority pie paradox”, we discussed the lack of relation between resource pools reservation settings and High Availability. As Craig so eloquently put it:
“RP reservations will not muck around with HA slot sizes”
High Availability ignores resource pools reservation settings when calculating the slot size, so if a single VM is placed in a resource pools with memory reservation configured, it will have the same effect on resource allocation as per VM memory reservation, but does not affect the HA slot size.
By creating a resource pool with a substantial memory setting you can avoid decreasing the consolidation ratio of the cluster and still guarantee the virtual machine its resources. Publishing this article does not automatically mean that I’m advocating using this workaround on a regular basis. I recommend implementing this workaround very sparingly as creating a RP for each VM creates a lot of administrative overhead and makes the host and cluster view a very unpleasant environment to work in.
A possible scenario to use this workaround can be when implementing MS Exchange 2010 mailbox servers. These mailbox servers are notorious for demanding a huge amount of memory and listed by many organizations as mission critical servers.
To emphasize it once more, this is not a best practice! But it might be useful in certain scenarios to avoid large slots and therefore low consolidation ratios.
Impact of host local VM swap on HA and DRS
On a regular basis I come across NFS based environments where the decision is made to store the virtual machine swap files on local VMFS datastores. Using host-local swap can affect DRS load balancing and HA failover in certain situations. So when designing an environment using host-local swap, some areas must be focused on to guarantee HA and DRS functionality.
VM swap file
Lets start with some basics, by default a VM swap file is created when a virtual machine starts, the formula to calculate the swap file size is: configured memory – memory reservation = swap file. For example a virtual machine configured with 2GB and a 1GB memory reservation will have a 1GB swap file.
Reservations will guarantee that the specified amount of virtual machine memory is (always) backed by ESX machine memory. Swap space must be reserved on the ESX host for the virtual machine memory that is not guaranteed to be backed by ESX machine memory. For more information on memory management of the ESX host, please the article on the impact of memory reservation.
During start up of the virtual machine, the VMkernel will pre-allocate the swap file blocks to ensure that all pages can be swapped out safely. A VM swap file is a static file and will not grow or shrink not matter how much memory is paged. If there is not enough disk space to create the swap file, the host admission control will not allow the VM to be powered up.
Note: If the local VMFS does not have enough space, the VMkernel tries to store the VM swap file in the working directory of the virtual machine. You need to ensure enough free space is available in the working directory otherwise the VM is still not allowed to be powered up. Let alone ignoring the fact that you initially didn’t want the VM swap stored on the shared storage in the first place.
This rule also applies when migrating a VM configured with a host-local VM swap file as the swap file needs to be created on the local VMFS volume of the destination host. Besides creating a new swap file, the swapped out pages must be copied out to the destination host. It’s not uncommon that a VM has pages swapped out, even if there is not memory pressure at that moment. ESX does not proactively return swapped pages back into machine memory. Swapped pages always stays swapped, the VM needs to actively access the page in the swap file to be transferred back to machine memory but this only occurs if the ESX host is not under memory pressure (more than 6% free physical memory).
Copying host-swap local pages between source- and destination host is a disk-to-disk copy process, this is one of the reasons why VMotion takes longer when host-local swap is used.
Real-life scenario
A customer of mine was not aware of this behavior and had discarded the multiple warnings of full local VMFS datastores on some of their ESX hosts. All the virtual machines were up and running and all seemed well. Certain ESX servers seemed to be low on resource utilization and had a few active VMs, while other hosts were highly utilized. DRS was active on all the clusters, fully automated and a default (3 stars) migration threshold. It looked like we had a major DRS problem.
DRS
If DRS decide to rebalance the cluster, it will migrate virtual machines to low utilized hosts. VMkernel tries to create a new swap file on the destination host during the VMotion process. In my scenario the host did not contain any free space in the VMFS datastore and DRS could not VMotion any virtual machine to that host because the lack of free space. But the host CPU active and host memory active metrics were still monitored by DRS to calculate the load standard deviation used for its recommendations to balance the cluster. (More info about the DRS algorithm can be found on the DRS deepdive page). The lack of disk space on the local VMFS datastores influenced the effectiveness of DRS and limited the options for DRS to balance the cluster.
High availability failover
The same applies when a HA isolation response occurs, when not enough space is available to create the virtual machine swap files, no virtual machines are started on the host. If a host fails, the virtual machines will only power-up on host containing enough free space on their local VMFS datastores. It might be possible that virtual machines will not power-up at-all if not enough free disk space is available.
Failover capacity planning
When using host local swap setting to store the VM swap files, the following factors must be considered.
• Amount of ESX hosts inside cluster.
• HA configured host failover capacity.
• Amount of active virtual machines inside cluster.
• Consolidation ratio (VM per host).
• Average swap file size.
• Free disk space local VMFS datastores.
Number of hosts inside cluster: | 6 |
HA configured host failover capacity: | 1 |
Active virtual machines: | 162 |
Average consolidation ratio: | 27:1 |
Average memory reservation: | 0GB |
Average swap file size: | 4GB |
For the sake of simplicity, let’s assume that DRS balanced the cluster load and that all (identical) virtual machines are spread evenly across every host.
In case of a host failure, 27 VMs will be restarted on the remaining 5 hosts inside the cluster, HA will start 5.4 virtual machines per host, as it is impossible to start 0.4 VM, some ESX hosts will start 6 virtual machines, while other hosts will start 5 VM’s.
The average swap file size is 4GB, this requires at least 24 GB of free space to be available on the local VMFS datastores to start the VM’s. Besides the 24GB, enough free space needs to be available to for DRS to move multiple VMs around to rebalance the load across the cluster.
If the design of the virtual infrastructure incorporates site failover as well, enough free disk space on all the ESX hosts must be reserved to power-up all the affected virtual machines from the failed site.
Closing remarks
Using host local swap can be a valid option for some environments, but additional calculation of the factors mentioned above is necessary to ensure sustained HA and DRS functionality.