All posts by Vikas Shitole

About Vikas Shitole

Vikas Shitole is a Senior Tech Lead at VMware by Broadcom, VCF division, India, where he leads system test efforts—including scale, stress, and resiliency testing—and drives product quality across VMware Cloud Foundation (VCF), Broadcom’s flagship private cloud platform. He is an AI and Kubernetes enthusiast, and is passionate about VMware customers and automation around vSphere and VCF. Vikas has been honoured as a vExpert for 13 consecutive years (2014–2026) for his sustained technical contributions and community leadership. He is the author of two VMware Flings, holds multiple industry certifications including VCF admin 9.0, and is one of the top contributors to the VMware API Sample Exchange, where his automation scripts have been downloaded over 50,000 times. Vikas has shared his expertise as a speaker at international conferences such as VMworld Europe and VMworld USA, and was selected as an official VMworld 2018 blogger. He also served as lead technical reviewer for the Packt-published books vSphere Design and VMware Virtual SAN Essentials. Beyond tech, Vikas is a dedicated cricketer, cycling enthusiast, and a lifelong learner in fitness and nutrition, with the personal goal of completing an Ironman 70.3

How to get datastore UUID across all the datastores in a vCenter server using vSphere APIs

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There are several vSphere API which requires datastore UUID as one of its important properties. Some examples of such APIs are MountVmfsVolume(),mountVmfsVolumeEx(), UnmountForceMountedVmfsVolume(), unmapVmfsVolumeEx(), DeleteVmfsVolumeState(), unmountVmfsVolumeEx & unmountVmfsVolume(). Without vmfsUUID we can NOT operate on any of these APIs. Now question is how to get/know the datastore UUID of the VMFS datastores connected to hosts using vSphere APIs. I thought its better to write the vSphere API script to solve this. The script that I am sharing is going to get all hosts and all of its associated datastores and each datastore and its associated UUID. Here we go.

[java]
package com.vmware.vijava;
import java.net.URL;
import java.util.HashMap;
import java.util.Map;
import java.util.Map.Entry;
import com.vmware.vim25.DatastoreInfo;
import com.vmware.vim25.VmfsDatastoreInfo;
import com.vmware.vim25.mo.Datastore;
import com.vmware.vim25.mo.Folder;
import com.vmware.vim25.mo.HostDatastoreBrowser;
import com.vmware.vim25.mo.HostSystem;
import com.vmware.vim25.mo.InventoryNavigator;
import com.vmware.vim25.mo.ManagedEntity;
import com.vmware.vim25.mo.ServiceInstance;
import com.vmware.vim25.mo.VirtualMachine;

public class findVMFSUUIDs {
public static void main(String[] args) throws Exception {
ServiceInstance si = new ServiceInstance(new URL(
"https://10.120.30.40/sdk"), "administrator@vsphere.local",
"Administrator!23", true);
// Get the rootFolder
Folder rootFolder = si.getRootFolder();

// Get all the hosts in the vCenter server
ManagedEntity[] hosts = new InventoryNavigator(rootFolder)
.searchManagedEntities("HostSystem");

if (hosts == null) {
System.out.println("Host not found on vCenter");
si.getServerConnection().logout();
return;
}

// Map to store the datastore name as key and its UUID as the value
Map< String , String> vmfsdatastoreUUIDs = new HashMap< String , String>();

// Map to store host as key and all of its datastores as the value
Map< ManagedEntity , Datastore[]> hostDatastores = new HashMap< ManagedEntity , Datastore[]>();
for (ManagedEntity hostSystem : hosts) {
HostDatastoreBrowser hdb = ((HostSystem) hostSystem)
.getDatastoreBrowser();
Datastore[] ds = hdb.getDatastores();
hostDatastores.put(hostSystem, ds);
}

System.out.println("Hosts and all of its associated datastores");
for (Map.Entry < ManagedEntity , Datastore[]> datastores : hostDatastores
.entrySet()) {
System.out.println("");
System.out.print("[" + datastores.getKey().getName() + "::");
for (Datastore datastore : datastores.getValue()) {
System.out.print(datastore.getName() + ",");
DatastoreInfo dsinfo = datastore.getInfo();
if (dsinfo instanceof VmfsDatastoreInfo) {
VmfsDatastoreInfo vdinfo = (VmfsDatastoreInfo) dsinfo;
vmfsdatastoreUUIDs.put(datastore.getName(), vdinfo
.getVmfs().getUuid());
}

}
System.out.print("]");
}
System.out.println(" ");
System.out.println("Datastore and its UUID");
for (Map.Entry< String , String> dsuuid : vmfsdatastoreUUIDs.entrySet()) {
System.out.println("[" + dsuuid.getKey() + "::" + dsuuid.getValue()
+ "]");
}

}
}
[/java]

Program Output::

Hosts and all of its associated datastores

[192.168.1.1::DS3,DS2,Local DS,DS4,NFS1,]
[192.168.1.2::LocalDS2,LocalDS1,LocalDS3,]

Datastore and its UUID

[LocalDS1::55d9b324-bad019fa-147a-f04da20356f7]
[Local DS::55b5dee1-ebd2895d-1263-002219574957]
[DS2::51e3e9f7-8ab3eac5-4715-00221957495d]
[LocalDS2::5592e051-da224dfc-ad16-f04da20356f7]
[LocalDS3::55d9b367-d145aef6-dcfe-f04da20356f7]
[DS3::51398f9b-e591008a-1b35-002219574a65]
[DS4::55cc410f-604db805-6fc0-002219574a65]
[/java]

Now you could enhance this code to operate on any vSphere API that requires VMFS UUID as one of parameters. Please do comment if you have any doubts or need any help.

If you have still not setup your VI JAVA Eclipse environment:Getting started tutorial
Important tutorials to start with: Part I & Part II

Note: VI JAVA open source project is forked into YA VIJAVA(Yet another VI JAVA), it will have support to all the new APIs introduced in vSphere 6.0. VI JAVA itself works fine even on vSphere 6.0 (except the new features in vSphere 6.0). Please do stay tuned my post on YA VIJAVA.

vSphere API using Java : Getting started/Samples consolidated

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Want to get started quickly with vSphere API using Java SDK coding but not getting easy to understand samples/tutorials? Want to get quick insight into vSphere APIs ? If you have these questions, this post is for you.

Some time back I had written 6 blog articles @vSphere API using Java SDK for VMware users and academic students. I thought to consolidate all the blog posts together in one short post so that it will be a one stop shop@vSphere API learning.

I recommend you to refer these 6 blog articles in below sequence. Blogs are written in a way that 1st year Engineering student should also understand easily.

Getting started with vSphere API using Java: How to quickly setup your eclipse environment (5-10 min)

Tutorial 1: How to initialize connection with ESXi host or vCenter server using vSphere API.

Tutorial 2:How to access/navigate vCenter server or ESXi host inventory using vSphere API

Sample 1: How to get datastore summary for all the datastores connected to a ESXi host.

Sample 2: How to create VM-Host DRS affinity rules using vSphere API

Sample 3: How to create VM-VM DRS affinity rules using vSphere API

You may also want to share these article with friends/students/customers. It will help them in their projects related to VMware. This also can be a trigger to start learning@VMware.

vSphere 6.0 : How SIOC works with Storage IO reservation

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Storage IO control(aka SIOC) is one of the cool vSphere features since it got introduced in vSphere 4.1. As we know, whenever there is IO resource contention on shared storage, Storage IO control (SIOC) balances IOPS across the VMs based on the IOPS shares allocated to each VMDK on the VM. As IOPS distribution done by SIOC was only based on IOPS shares , in case of IO contention, there was not IOPS distribution guarantee for mission critical VMs. Also it was hard for admins to understand IO intensive worklaods and set the appropriate IOPS shares, in some cases even admin needs to re-calculate IOPS shares allocation based on workloads. Already there is increasing trend of moving tier-1 applications (which requires guaranteed performance) to vSphere. In order to take this trend of moving tier-1 applications on vSphere platform to the next level, it was very critical that SIOC also should consider storage IO reservation to satisfy service level agreements. I am really excited that VMware has added this feature as part of vSphere 6.0, with vSphere 6.0, SIOC is going to obey the storage IO reservation as well in terms of IOPS whenever there is IO contention. This is going to ensure the guaranteed performance in terms of IOPS. As part of vSphere beta program, I got chance to play with this feature & I would say this feature also going to ensure the greater level of IO performance isolation when admin puts more number of VMDKs per LUN. Now in addition to shares & limit, we have “reservation” as well to manage tier-1 IO(disk) intensive workloads using SIOC. It is important again to note that SIOC will only get invoked when there is IO contention ( based on set IO threshold in terms of latency)

In this post we will see couple of examples on how Storage IO reservation works with Storage IO control (SIOC).

Example 1:
Initial setup
-Say, We have 2 ESXi hosts (H1,H2) with 1 VM per host i.e. H1-VM1 & H2-VM2. Both host are attached to shared datastore DS1. i.e. Both VMs are on the datastore DS1( SIOC is enabled).

IOPS Shares/Reservation Configuration
-Initially each VM takes default share values i.e. Normal (say 1000). Hence VM1 & VM2 will have 1000 shares per VM.
-Consider max IOPS capacity of the shared datastore DS1 is 100 & we set IOPS reservation on VM1 as 60 and IOPS reservation on VM2 is NOT set.

How SIOC will behave
-In case IO contention, based on shares VM1 and VM2 will get 50 IOPS each as their shares are 1000 each but if we notice bit closer IOPS reservation set on the VM1 itself is 60, what happens now? When IOPS distribution based on shares per VM is less than the IO reservation set on the VM (this is our case), IOPS distributed to the VM will be based on IOPS reservation. Hence in our case, VM1 will get 60 IOPS and VM2 will get remaining IOPS i.e. 40.
– If reservation would not have there, each VM would have got IOPS in 1:1 ratio based on shares.

Example 2:
Initial setup is same as that of Example 1.

IOPS Shares/Reservation Configuration
– Share value set on VM1 is 500 & VM2 is 2000.
-Consider max IOPS capacity of the shared datastore DS1 is 100 & we set IOPS reservation on VM1 as 50 and IOPS reservation on VM2 is NOT set.

How SIOC will behave
-In case IO contention, based on shares VM1 will get IOPS distribution as 20 & and VM2 will get 80 but if we notice bit closer IOPS reservation set on the VM1 itself is 50, what happens now? When IOPS distribution based on shares per VM is less than the IO reservation set on the VM (this is our case), IOPS distributed to the VM will be based on IOPS reservation. Hence in our case, VM1 will get 50 IOPS and VM2 will get remaining IOPS i.e. 50.

My additional observations:
-When IOPS reservation set across VMs is more than the max IOPS capacity of the shared datastore, In case of IO contention, IOPS gets distributed in the ratio of set IOPS reservation.
-If IOPS calculation for particular VM based on share is more than the IOPS limit set on the same VM, that will IOPS upto limit set. SIOC will make sure IOPS distribution can not be more than the IOPS limit set on the VM.
-We can not set IOPS reservation more than the IOPS limit.

This is how, in case of IO contention, SIOC will make sure IOPS distribution across VM is guaranteed. I hope you enjoyed reading this post. Stay tuned for my next post on how to configure SIOC and storage IO reservation from vSphere API or web client.

In order to understand SIOC in detail, please refer SIOC detailed insight

PART 2: vSphere DPM vs ESXi memory ballooning

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In PART I of my post we learned how vSphere DPM works & DPM memory demand metric. If you have not read PART I, I strongly recommend reading it first.

In PART 2 we will touch upon 2 important points with example.
1. How earlier vSphere DPM behavior was more aggressive from memory perspective?
2. How can we now control DPM behavior with new memory demand metric & avoid memory ballooning?

Let’s start with first point: How earlier DPM behavior was more aggressive?
OLD DPM memory demand metric was considering just Active memory as memory demand. To understand it clearly, we will take one example: Say we have 2 ESXi hosts(H1 & H2) with 2 VMs on each hosts(VM1,VM2 on H1 & VM3, VM4 on H2) in the DPM enabled cluster. Memory configuration was as follows:
H1-4GB
H2-4GB
VM1-3GB
VM2-3GB
VM3-3GB
VM4-3GB
Clearly environment is memory over-committed. All VMs are already powered ON, host consumed memory on H1 =3 GB, H2=1GB & current active memory usage on each VM is just 256MB. You may wonder that why host consumed memory is 3 GB on H1 when current active memory on H1 is just 512 MB(256x2VMs). The reason is, initially active memory usage for VMs on host H1 was 3GB but at this point active memory usage on H1 reduced to 512MB & as per the ESXi memory management, unused memory on the VMs will not freed itself to host until ESXi does not use its memory reclamation technique such as memory ballooning. Memory ballooning reclaims the VM memory only when host memory usage crosses 96% of its total host memory.

DPM will evaluate the cluster based on Target Resource Utilization Range = 63±18 i.e. Default range is 45 to 81%. As per active memory usage on both hosts, it is clear that only 1GB(256×4 VMs) memory is being used actively in the cluster which is just around 12.5% of the total memory (4 GB) available per host. Each ESXi host’s resource utilization demand is calculated as aggregate of memory required by VMs running on that host. Based on Target utilization range DPM identifies one of hosts as candidate host (in our case H2) to put into standby mode. DPM runs DRS simulation on the remaining one host i.e. H1 (DRS simulation will not consider the candidate hosts to be powered OFF, in our case it is H2), simulation uses the DPM demand metric formula i.e. just active memory to analyze whether VMs(VM3,VM4) on candidate host H2 can be accommodated on host H1 without impacting existing VMs(VM1,VM2). As the overall active memory across all the VMs is 1GB (256×4 VMs) & 1GB is just 25% of the memory utilization of H1 which is too less than upper target utilization range i.e. 81. (Before putting host into standby mode, DPM also makes sure that remaining host memory utilization should not cross the upper utilization range i.e. 81%). Hence DPM will vMotion VMs (with the help of DRS) from candidate host H2 to H1 and will put H2 into standby mode to save the power consumption. Note that by this time host consumed memory would be at-least 3 GB(earlier)+512 MB(VMs on H2 those are migrated to H1)=3.5 GB. If say, suddenly memory demand for recently migrated VMs increased by even 200MB each, host consumed memory on H1 would cross 96% of total memory available. This is where host H1 is very short on memory & it will immediately start memory ballooning in order to reclaim the unused VM memory to satisfy the memory demand by VMs. It is clear that old DPM memory demand metric did not consider future memory demand growth on any VMs those are currently on H1. Memory ballooning itself will not cause the performance impact as balloon driver will first reclaim the guest memory which is unused by guest OS which is perfectly safe (More on ballooning in next post). If memory is excessively over-committed & memory reclaimed by ballooning is not enough, it can lead to host swapping and it severally impacts the performance of the VMs.

Note: For the sake of simplicity in examples I did not consider memory required for virtualization layer.

Now you will be wondering, does not DPM evaluate(in above case) cluster to bring back the standby host H2 to meet the memory demand? Of course YES but as DPM evaluates hosts for power ON recommendation every 5 min, DPM will wait to complete the 5 min. As soon as DPM is invoked, DPM evaluates the cluster to bring back the standby host to meet the memory demand, once the standby host is powered ON, DRS balances the memory load in the cluster and it will stop the memory ballooning. However, hosts in the cluster may come across memory ballooning for the minimum time ranges from 5 min i.e. DPM invocation time for power on host recommendations + time DPM takes to evaluate the hosts + time required for the host to boot up from standby mode + Time required to vMotion as a result of balancing the memory load.

Overall, with DPM’s old memory demand metric DPM may lead to memory ballooning when active memory is low but host consumed memory is high. Host consumed memory can be high with low active memory when allocated VMs memory is either overcommitted OR it can even happen when VMs memory is fully backed by physical memory.

At this point I assume that you clearly understood that how earlier memory demand metric (active memory) was very aggressive.

Now it is time to see, how can we now control DPM behavior with new memory demand metric?
In order to control DPM’s aggressiveness, from vCenter 5.1 U2c onwards and all the versions of vCenter 5.5, DPM can be tuned to consider idle consumed memory as well in DPM memory demand metric. i.e. new DPM memory demand metric = active memory + X% of idle consumed memory. Default value of the X is 25. X value can be modified by using DRS advanced option “ PercentIdleMBInMemDemand” on cluster level. We can set this value in the range from 0 to 100. Refer this KB on how to configure PercentIdleMBInMemDemand advanced option.

We will continue the same above example:
We set “PercentIdleMBInMemDemand” option to 100 i.e. X value is 100. Initially on H1 host consumed memory was 3 GB & active memory usage was 512MB (256×2 VMs) and on H2 host consumed memory was 1 GB & active memory usage was 512MB(256×2 VMs). In this case when DPM evaluates the cluster, as host H2 has less than 45% memory usage, DPM picks host H2 as candidate host in order to put H2 into standby mode. DPM runs DRS simulation without considering the host H2 as it is a identified candidate host to be put into standby mode. DRS simulation uses DPM new demand memory metric i.e. Active memory + X% of idle consumed memory. Active memory on H1 is 512 MB, hence idle consumed memory on H1 is 3GB-512 MB=2.5GB. It shows that memory demand on H1 is 3 GB as X is 100. Active memory usage for the VMs on H2 those are going to be migrated to H1 (only if DPM finalizes to put H2 into standby mode) is 512 MBs, hence idle consumed memory on H2 is 1GB-512 MB=512 MB, it shows memory demand for VMs on H2 is 1 GB as X is 100. Finally, total memory demand by DPM is 4GB (3GB from VMs on H1 and 1 GB from VMs on H2), 4GB memory demand is way out of memory utilization target range i.e. 81%. As total memory demand by all the VMs goes out of utilization target range(Default range 45%-81%), DPM does not see any value in putting H2 into standby mode as for DPM performance is preference than saving power consumption, hence DPM will not put any host into standby mode & consequently avoids memory ballooning. This example shows that when there is high consumed memory and low active memory usage in your environment, it is better to set DRS advanced option PercentIdleMBInMemDemand to 100.

Note: DRS memory demand metric also uses the same formula but in this post I have just focused on DPM.

Now I am sure that you understood how DPM’s new memory demand metric can be used to fine tune DPM behavior which in turn can help to avoid memory ballooning.

is there direct relationship between new DPM memory demand metric and ESXi host memory ballooning. Answer is NO. There is NO direct relationship between new DPM memory demand metric and VM memory ballooning. New memory demand metric just gives us configurable option to fine tune DPM to consider more consumed memory as future memory demand while making host power ON/Off decisions. This would keep more memory resources available in cluster. Hence, it should indirectly avoid the ballooning.
It is also important to note that, DPM will not power on the standby host only because ballooning is happening on other host in the cluster as there is no direct relation between ballooning and DPM. In this case as well, when DPM gets invoked, it will check the target utilization range and only if host memory utilization exceeds the range, it starts evaluating the standby host based on memory demand formula (active memory + X% of idle consumed memory) in order to take hosts out of standby mode.
However, memory ballooning on VMs may happen (when host(s) are in standby mode) very rarely as DPM already would have considered conservative X% value before putting hosts into standby mode i.e. DPM would have kept enough memory resources(of course it is depend on the value of X i.e. PercentIdleMBInMemDemand) available in the cluster before putting any host into standby. Even then if ballooning happens, it is mean that there is excessive memory over commitment and/or actual memory demand by powered ON VMs is more than anticipated by DPM (using active + X% idle consumed).

Can actual memory demand of powered ON VMs be more than anticipated by DPM (which was based on new demand metric)? Yes, it can happen in very rare cases that too due to highly unpredictable increase in memory workloads/usage . Ex. Say cluster has 2 hosts(H1 and H2) with 1 VM on each. Consider, VM on a H1 has 8GB memory allocated but only 3GB is consumed by VM at the moment & X is set to 100. If X is 100, DPM considers entire consumed memory as memory demand. Based on 3GB memory demand, DPM puts host into standby mode (consider 3 GB is available on other host H2) but unfortunately, the moment DPM put the host into standby mode, memory demand of the VM got increased & consumed memory for the VM reached to say 7GB (very corner case) which is 4GB more than DPM had just anticipated. Now if host H2 does not have memory to satisfy this memory demand, it can lead to ballooning. However, once DPM realizes that memory utilization range is exceeding the target utilization range, it again evaluates cluster to bring back the standby host. It is worth to note that if VMs shares,limits & reservations are misconfigured, it can lead to ballooning even if there is plenty of memory available on host. (More on this in next post).

I hope you enjoyed DPM memory behavior, please leave the comment if you have any query. Stay tuned for PART 3 post on ESXi memory ballooning & memory best practices.

If you want to have even more depth understanding of DPM, please refer below resources
1. White Paper on DPM by VMware
2. Great book by “Duncan & Frank”: VMware vSphere 5.1 Clustering Deepdive

PART 1: vSphere DPM vs ESXi memory ballooning

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One question is always getting popped into my inbox , the question is: can vSphere DPM lead to ESXi host memory ballooning? if yes, is there any way we can fine tune vSphere DPM to avoid the memory ballooning? I explained it whenever possible but when this question keeps popping up again and again, I thought its better to write one posts to give overview of the vSphere DPM, its memory demand metric and how memory ballooning relates to DPM.

I have divided this post in 3 parts as follows
Part 1: DPM basic overview & its memory demand metric calculations.
Part 2 : DPM vs Memory ballooning & memory best practices.
Part 3: What are the ways we can fine tune DPM?

Today I have covered Part 1 : “DPM basic overview & its memory demand metric calculations”.

DPM basic overview:
As we already know that consolidation of physical servers into virtual machines reduces significant power consumption. VMware DPM (Distributed Power Management) takes this reduction in power consumption to the next level.
DPM is feature of VMware DRS (Distributed Resource Scheduler), once we enable DRS on cluster from vSphere client or Web client, enabling DPM is just a click away. DRS does dynamic CPU and memory load balancing across all ESXi in the cluster & DPM does the evaluation of each ESXi host in the cluster so that DPM can put one or more hosts into standby mode (Power OFF) to save the power consumption OR bring back one or more hosts from standby mode to meet the resource (cpu, memory) demand of virtual machines in the cluster. You might be wondering how DPM evaluates ESXi host? It is the Target Resource utilization Range that plays the crucial role. DPM calculates Target Resource Utilization Range as follows.

Target Resource Utilization Range = DemandCapacityRatioTarget ±
DemandCapacityRatioToleranceHost

DemandCapacityRatioTarget is the target utilization of the ESXi host in the cluster. By default this is set at 63%.
DemandCapacityRatioToleranceHost sets the tolerance value around target utilization of each ESXi host, by default this is set at 18%.
Hence, by default, Target Resource Utilization Range = 63±18 i.e. Range is 45 to 81%

It is mean that DPM try its best to keep the ESXi host resource utilization in the range between 45 and 81 percent. If resource utilization of cpu or memory on each ESXi host is below 45%, DPM evaluates that host for putting into standby mode (Power OFF). If the resource utilization exceeds the 81% of either CPU or memory resources, DPM evaluates ESXi host to bring back that hosts from standby mode.
Note: DPM considers CPU & memory as resource for evaluation however, In this blog post, we would be focusing only on memory resource.

Basic terms:
Active memory: Memory which is being actively used at any point of moment by VM. This keeps changing as the VM load increases or decreases.

Consumed memory: It is the memory consumed by VM since it is booted. Note that consumed memory is not the same as memory allocated to VM. Consumed memory can be equal to allocated (configured) memory if & only if VM consumes entire memory allocated to VM. ESXi host never allocates memory to any VM until that VM touches/requests the host memory. When VM is powered off consumed memory would be zero. It is good to note that every VM only can get min(Configured memory, specified limit). When VM is powered OFF, consumed memory would be zero. If there is no any limit set on VM, configured memory itself will be default limit.

DPM memory demand metric.

In earlier releases, DPM was just considering active memory as memory demand from each VM on the host. i.e. “DPM memory demand metric=active memory” which is aggressive. In order to control DPM’s aggressiveness, with version vCenter 5.1 U2c onwards and all the versions of vCenter 5.5, DPM can be tuned to consider idle consumed memory as well in DPM memory demand metric. i.e. DPM memory demand metric = active memory + X% of idle consumed memory.

1. Default value of the X is 25. X value can be modified by using DRS advanced option “ PercentIdleMBInMemDemand” on cluster level. We can set this value in the range from 0 to 100. When we set this value to 0, it is mean that DPM will be aggressive the way it was in earlier release & as we increase X value DPM keeps becoming less aggressive. If X value is 100, it is mean that DPM considers entire consumed memory as memory demand. (Consumed memory =active memory + idle consumed memory).

2. Example. : Say, we have one VM with 8192MB (8GB) configured memory. Consider since the VM is booted, VM has consumed 6144MB (6GB) memory from host but only 20% is being used actively, hence active memory would be 20% of 6144 MB=1228.8 MB. Idle consumed memory =6144-1228.8=4915.2 MB. If X value is 25 then DPM memory demand would be=1228.8 + 25 % of 4915.2 =2457.6 MB + overhead. Setting X to 25 means, DPM considers 25% of idle consume memory as a demand by VM to avoid performance impact. As we increase X, DPM becomes more conservative. Hence user needs to set the X value as per his environment & requirement.

3. DPM Power OFF recommendations: Based on Target Resource utilization range, DPM evaluates candidate hosts to put into standby mode (i.e. When utilization is under 45%), and then DPM takes help from DRS to run the simulations considering candidate hosts are powered off in the cluster. These DRS simulations internally use the DPM memory demand metric (active memory + X% of idle consumed memory) to calculate the memory demand by each VM in the cluster. These simulations will be used by DPM to see if there is improvement in Target Resource Utilization Range when candidate host(s) is powered OFF. If resource utilization of the all non-candidate hosts is within the target range (i.e. 45%-81%), DPM puts the candidate hosts into standby mode & saves the power.

4. DPM Power ON recommendations: DPM evaluates each standby host when resource utilization of the powered ON host is above 81%, and then DPM takes help from DRS to run the simulations considering standby host(s) is powered ON in the cluster. These DRS simulations internally use the DPM memory demand metric (active memory + X% of idle consumed memory) to calculate the memory demand by each VM in the cluster & distributes the VMs across all hosts. These simulations will be used by DPM to see if there is improvement in Target Resource Utilization Range when standby host(s) is powered-on. If resource utilization of the all hosts is within the target range, DPM generates host power ON recommendations.

I hope you enjoyed how DPM works in general, please do leave comment for any clarification & stay tuned for exciting PART 2 “DPM vs Memory ballooning & memory best practices.”

If you want to have even more depth understanding of DPM, please refer below resources
1. White Paper on DPM by VMware
2. Great book by “Duncan & Frank”: VMware vSphere 5.1 Clustering Deepdive