Home > Articles

  • Print
  • + Share This
This chapter is from the book

Distributed Resource Scheduler (DRS)

DRS distributes compute workload in a cluster by strategically placing virtual machines during power-on operations and live migrating (vMotion) VMs when necessary. DRS provides many features and settings that enable you to control its behavior.

You can set DRS Automation Mode for a cluster to one of the following:

  • Manual: DRS does not automatically place or migrate virtual machines. It only makes recommendations.

  • Partially Automated: DRS automatically places virtual machines as they power on. It makes recommendations for virtual machine migrations.

  • Fully Automated: DRS automatically places and migrates virtual machines.

You can override Automation Mode at the virtual machine level.

Recent DRS Enhancements

VMware added many improvements to DRS beginning in vSphere 6.5. For example, in vSphere 7.0, DRS runs once every minute rather than every 5 minutes, as in older DRS versions. The newer DRS versions tend to recommend smaller (in terms of memory) virtual machines for migration to facilitate faster vMotion migrations, whereas older versions tend to recommend large virtual machines to minimize the number of migrations. Older DRS versions use an imbalance metric that is derived from the standard deviation of load across the hosts in the cluster. Newer DRS versions focus on virtual machine happiness. Newer DRS versions are much lighter and faster than the older versions.

Newer DRS versions recognize that vMotion is an expensive operation and account for it in their recommendations. In a cluster where virtual machines are frequently powered on and the workload is volatile, it is not necessary to continuously migrate virtual machines. DRS calculates the gain duration for live migrating a virtual machine and considers the gain duration when making recommendations.

The following sections provide details on other recent DRS enhancements.

Network-Aware DRS

In vSphere 6.5, DRS considers the utilization of host network adapters during initial placement and load balancing, but it does not balance the network load. Instead, its goal is to ensure that the target host has sufficient available network resources. It works by eliminating hosts with saturated networks from the list of possible migration hosts. The threshold used by DRS for network saturation is 80% by default. When DRS cannot migrate VMs due to network saturation, the result may be an imbalanced cluster.

In vSphere 7.0, DRS uses a new cost modeling algorithm that is flexible and balances network bandwidth along with CPU and memory usage.

Virtual Machine Distribution

Starting in vSphere 6.5, you can enable an option to distribute a more even number of virtual machines across hosts. The main use case for this is to improve availability. The primary goal of DRS—to ensure that all VMs are getting the resources they need and that the load is balanced in the cluster—remains unchanged. But with this new option enabled, DRS also tries to ensure that the number of virtual machines per host is balanced in the cluster.

Memory Metric for Load Balancing

Historically, vSphere has used the Active Memory metric for load-balancing decisions. In vSphere 6.5 and 6.7, you have the option to set DRS to balance the load based on the Consumed Memory metric. In vSphere 7.0, the Granted Memory metric is used for load balancing, and no cluster option is available to change the behavior.

Virtual Machine Initial Placement

Starting with vSphere 6.5, DRS uses a new initial placement algorithm that is faster, lighter, and more effective than the previous algorithm. In earlier versions, DRS takes a snapshot of the cluster state when making virtual machine placement recommendations. In the algorithm, DRS does not snapshot the cluster state, which allows for faster and more accurate recommendations. With the new algorithm, DRS powers on virtual machines much more quickly. In vSphere 6.5, the new placement feature is not supported for the following configurations:

  • Clusters where DPM, Proactive HA, or HA Admission Control is enabled

  • Clusters with DRS configured in Manual Mode

  • Virtual machines with the Manual DRS Override setting enabled

  • Virtual machines that are FT enabled

  • Virtual machines that are part of a vApp

In vSphere 6.7, the new placement is available for all configurations.

Enhancements to the Evacuation Workflow

Prior to vSphere 6.5, when evacuating a host entering Maintenance Mode, DRS waited to migrate templates and powered off virtual machines until after the completion of vMotion migrations, leaving those objects unavailable for use for a long time. Starting in vSphere 6.5, DRS prioritizes the migration of virtual machine templates and powered-off virtual machines over powered-on virtual machines, making those objects available for use without waiting on vMotion migrations.

Prior to vSphere 6.5, the evacuation of powered-off virtual machines was inefficient. Starting in vSphere 6.5, these evacuations occur in parallel, making use of up to 100 re-register threads per vCenter Server. This means that you may see only a small difference when evacuating up to 100 virtual machines.

Starting in vSphere 6.7, DRS is more efficient in evacuating powered-on virtual machines from a host that is entering Maintenance Mode. Instead of simultaneously initiating vMotion for all the powered-on VMs on the host, as in previous versions, DRS initiates vMotion migrations in batches of eight at a time. Each vMotion batch is issued after the previous batch completes. The vMotion batching makes the entire workflow more controlled and predictable.

DRS Support for NVM

Starting in vSphere 6.7, DRS supports virtual machines running on next-generation persistent memory devices, known as non-volatile memory (NVM) devices. NVM is exposed as a datastore that is local to the host. Virtual machines can use the datastore as an NVM device exposed to the guest (Virtual Persistent Memory [vPMem]) or as a location for a virtual machine disk (Virtual Persistent Memory Disk [vPMemDisk]). DRS is aware of the NVM devices used by virtual machines and guarantees that the destination ESXi host has enough free persistent memory to accommodate placements and migrations.

How DRS Scores VMs

Historically, DRS balanced the workload in a cluster based on host compute resource usage. In vSphere 7.0, DRS balances the workload based on virtual machine happiness. A virtual machine’s DRS score is a measure of its happiness, which, in turn, is a measure of the resources available for consumption by the virtual machine. The higher the DRS score for a VM, the better its resource availability. DRS moves virtual machines to improve their DRS scores. DRS also calculates a DRS score for a cluster, which is a weighted sum of the DRS scores of all the virtual machines in the cluster.

In Sphere 7.0, DRS calculates the core for each virtual machine on each ESXi host in the cluster every minute. Simply put, DRS logic computes an ideal throughput (demand) and an actual throughput (goodness) for each resource (CPU, memory, and network) for each virtual machine. The virtual machine’s efficiency for a particular resource is a ratio of the goodness over the demand. A virtual machine’s DRS score (total efficiency) is the product of its CPU, memory, and network efficiencies.

When calculating the efficiency, DRS applies resource costs. For CPU resources, DRS includes costs for CPU cache, CPU ready, and CPU tax. For memory resources, DRS includes costs for memory burstiness, memory reclamation, and memory tax. For network resources, DRS includes a network utilization cost.

DRS compares a virtual machine’s DRS score for the host on which it currently runs. DRS determines whether another host can provide a better DRS score for the virtual machine. If so, DRS calculates the cost for migrating the virtual machine to the host and factors that score into its load-balancing decision.

DRS Rules

You can configure rules to control the behavior of DRS.

A VM–host affinity rule specifies whether the members of a selected virtual machine DRS group can run on the members of a specific host DRS group. Unlike a virtual machine–to–virtual machine (VM–VM) affinity rule, which specifies affinity (or anti-affinity) between individual virtual machines, a VM–host affinity rule specifies an affinity relationship between a group of virtual machines and a group of hosts. There are required rules (designated by “must”) and preferential rules (designated by “should”).

A VM–host affinity rule includes the following components:

  • One virtual machine DRS group

  • One host DRS group

  • A designation of whether the rule is a requirement (“must”) or a preference (“should”) and whether it is affinity (“run on”) or anti-affinity (“not run on”)

A VM–VM affinity rule specifies whether selected individual virtual machines should run on the same host or be kept on separate hosts. This type of rule is used to create affinity or anti-affinity between individual virtual machines. When an affinity rule is created, DRS tries to keep the specified virtual machines together on the same host. You might want to do this, for example, for performance reasons.

With an anti-affinity rule, DRS tries to keep the specified virtual machines apart. You can use such a rule if you want to guarantee that certain virtual machines are always on different physical hosts. In that case, if a problem occurs with one host, not all virtual machines are at risk. You can create VM–VM affinity rules to specify whether selected individual virtual machines should run on the same host or be kept on separate hosts.

VM–VM affinity rule conflicts can occur when you use multiple VM–VM affinity and VM–VM anti-affinity rules. If two VM–VM affinity rules are in conflict, you cannot enable both of them. For example, if one rule keeps two virtual machines together and another rule keeps the same two virtual machines apart, you cannot enable both rules. Select one of the rules to apply and disable or remove the conflicting rule. When two VM–VM affinity rules conflict, the older one takes precedence, and the newer rule is disabled. DRS tries to satisfy only enabled rules and ignores disabled rules. DRS gives higher precedence to preventing violations of anti-affinity rules than violations of affinity rules.

DRS Migration Sensitivity

Prior to vSphere 7.0, DRS used a migration threshold to determine when virtual machines should be migrated to balance the cluster workload. In vSphere 7.0, DRS does not consider cluster standard deviation for load balancing. Instead, it is designed to be more virtual machine centric and workload centric rather than cluster centric. You can set the DRS Migration Sensitivity parameter to one of the following values:

  • Level 1: DRS only makes recommendations to fix rule violations or to facilitate a host entering Maintenance Mode.

  • Level 2: DRS expands on Level 1 by making recommendations in situations that are at or close to resource contention. It does not make recommendations just to improve virtual machine happiness or cluster load distribution.

  • Level 3: DRS expands on Level 2 by making recommendations to improve VM happiness and cluster load distribution. This is the default level.

  • Level 4: DRS expands on Level 3 by making recommendations for occasional bursts in the workload and reacts to sudden load changes.

  • Level 5: DRS expands on Level 4 by making recommendations dynamic and greatly varying workloads. DRS reacts to the workload changes every time.

Resource Pools

Resource pools are container objects in the vSphere inventory that are used to compartmentalize the CPU and memory resources of a host, a cluster, or a parent resource pool. Virtual machines run in and draw resources from resource pools. You can create multiple resource pools as direct children of a standalone host or a DRS cluster. You cannot create child resource pools on a host that has been added to a cluster or on a cluster that is not enabled for DRS.

You can use resource pools to organize VMs. You can delegate control over each resource pool to specific individuals and groups. You can monitor resources and set alarms on resource pools. If you need a container just for organization and permission purposes, consider using a folder. If you also need resource management, then consider using a resource pool. You can assign resource settings such as shares, reservations, and limits to resource pools.

Use Cases

You can use resource pools to compartmentalize a cluster’s resources and then use the resource pools to delegate control to individuals or organizations. Table 4-4 provides some use cases for resource pools.

Table 4-4 Resource Pool Use Cases

Use Case

Details

Flexible hierarchical organization

Add, remove, modify, and reorganize resource pools, as needed.

Resource isolation

Use resource pools to allocate resources to separate departments, in such a manner that changes in a pool do not unfairly impact other departments.

Access control and delegation

Use permissions to delegate activities, such as virtual machine creation and management, to other administrators.

Separation of resources from hardware

In a DRS cluster, perform resource management independently of the actual hosts.

Managing multitier applications.

Manage the resources for a group of virtual machines (in a specific resource pool), which is easier than managing resources per virtual machine.

Shares, Limits, and Reservations

You can configure CPU and memory shares, reservations, and limits on resource pools, as described in Table 4-5.

Table 4-5 Shares, Limits, and Reservations

Option

Description

Shares

Shares specify the relative importance of a virtual machine or a resource pool. If a virtual machine has twice as many shares of a resource as another virtual machine, it is entitled to consume twice as much of that resource when these two virtual machines are competing for resources. Shares can be thought of as priority under contention.

Shares are typically set to High, Normal, or Low, and these values specify share values with a 4:2:1 ratio. You can also select Custom and assign a specific number of shares (to express a proportional weight).

A resource pool uses its shares to compete for the parent’s resources and is allocated a portion based on the ratio of the pool’s shares compared with its siblings. Siblings share the parent’s resources according to their relative share values, bounded by the reservation and limit.

For example, consider a scenario where a cluster has two child resource pools with normal CPU shares, another child resource pool with high CPU shares, and no other child objects. During periods of contention, each of the pools with normal shares would get access to 25% of the cluster’s CPU resources, and the pool with high shares would get access to 50%.

Reservations

A reservation specifies the guaranteed minimum allocation for a virtual machine or a resource pool. A CPU reservation is expressed in megahertz, and a memory reservation is expressed in megabytes. You can power on a virtual machine only if there are enough unreserved resources to satisfy the reservation of the virtual machine. If the virtual machine starts, then it is guaranteed that amount, even when the physical server is heavily loaded.

For example, if you configure the CPU reservation for each virtual machine as 1 GHz, you can start eight VMs in a resource pool where the CPU reservation is set for 8 GHz and expandable reservations are disabled. But you cannot start additional virtual machines in the pool.

You can use reservations to guarantee a specific amount of resources for a resource pool. The default value for a resource pool’s CPU or memory reservation is 0. If you change this value, it is subtracted from the unreserved resources of the parent. The resources are considered reserved, regardless of whether virtual machines are associated with the resource pool.

Expandable reservations

You can enable expandable reservations to effectively allow a child resource pool to borrow from its parent. Expandable reservations, which are enabled by default, are considered during admission control. When powering on a virtual machine, if the resource pool does not have sufficient unreserved resources, the resource pool can use resources from its parent or ancestors.

For example, say that in a resource pool where 8 GHz is reserved and expandable reservations is disabled, you try to start nine virtual machines each with 1 GHz, but the last virtual machine does not start. If you enable expandable reservation in the resource pool, and its parent pool (or cluster) has sufficient unreserved CPU resources, you can start the ninth virtual machine.

Limits

A limit specifies an upper bound for CPU or memory resources that can be allocated to a virtual machine or a resource pool.

You can set a limit on the amount of CPU and memory allocated to a resource pool. The default is unlimited. For example, if you power on multiple CPU-intensive virtual machines in a resource pool, where the CPU limit is 10 GHz, then, collectively, the virtual machines cannot use more than 10 GHz CPU resources, regardless of the pool’s reservation settings, the pool’s share settings, or the amount of available resources in the parent.

Table 4-6 provides the CPU and memory share values for virtual machines when using the High, Normal, and Low settings. The corresponding share values for a resource pool are equivalent to those of a virtual machine with four vCPUs and 16 GB memory.

Table 4-6 Virtual Machine Shares

Setting

CPU Share Value

Memory Share Value

High

2000 per vCPU

20 per MB

Normal

1000 per vCPU

10 per MB

Low

500 per vCPU

5 per MB

For example, the share values for a resource pool configured with normal CPU shares and high memory shares are 4000 (that is, 4 × 1000) CPU shares and 327,680 (that is, 16 × 1024 × 20) memory shares

Enhanced Resource Pool Reservation

Starting in vSphere 6.7, DRS uses a new two-pass algorithm to allocate resource reservations to children. The old allocation model does not reserve more resources than the current demand, even when the resource pool is configured with a higher reservation. When a spike in virtual machine demand occurs after resource allocation is complete, DRS does not make the remaining pool reservation available to the virtual machine until the next allocation operation occurs. As a result, a virtual machine’s performance may be temporarily impacted. In the new allocation model, each allocation operation uses two passes. In the first pass, the resource pool reservation is allocated based on virtual machine demand. In the second pass, excess pool reservation is allocated proportionally, limited by the virtual machine’s configured size, which reduces the performance impact due to virtual machine spikes.

Scalable Shares

Another new DRS feature in vSphere 7.0 is scalable shares. The main use case for scalable shares is a scenario in which you want to use shares to give high-priority resource access to a set of virtual machines in a resource pool, without concern for the relative number of objects in the pool compared to other pools. With standard shares, each pool in a cluster competes for resource allocation with its siblings, based on the share ratio. With scalable shares, the allocation for each pool factors in the number of objects in the pool.

For example, consider a scenario in which a cluster with 100 GHz CPU capacity has a high-priority resource pool with CPU Shares set to High and a low-priority resource pool with CPU Shares set to Normal, as shown in Figure 4-1. This means that the share ratio between the pools is 2:1, so the high-priority pool is effectively allocated twice the CPU resources as the low-priority pool whenever CPU contention exists in the cluster. The high-priority pool is allocated 66.7 GHz, and the low-priority pool is effectively allocated 33.3 GHz. In this cluster, 40 virtual machines of equal size are running, with 32 in the high-priority pool and 8 in the low-priority pool. The virtual machines are all demanding CPU resources, causing CPU contention in the cluster. In the high-priority pool, each virtual machine is allocated 2.1 GHz. In the low-priority pool, each virtual machine is allocated 4.2 GHz.

FIGURE 4-1

FIGURE 4-1 Scalable Shares Example

If you want to change the resource allocation such that each virtual machine in the high-priority pool is effectively allocated more resources than the virtual machines in the low-priority pool, you can use scalable shares. If you enable scalable shares in the cluster, DRS effectively allocates resources to the pools based on the Shares settings and the number of virtual machines in the pool. In this example, the CPU shares for the pools provide a 2:1 ratio. Factoring this with the number of virtual machines in each pool, the allocation ratio between the high-priority pool and the low-priority pool is 2 times 32 to 1 times 8, or simply 8:1. The high-priority pool is allocated 88.9 GHz, and the low-priority pool is allocated 11.1 GHz. Each virtual machine in the high-priority pool is allocated 2.8 GHz. Each virtual machine in the low-priority pool is allocated 1.4 GHz.

  • + Share This
  • 🔖 Save To Your Account

Pearson IT Certification Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from Pearson IT Certification and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about Pearson IT Certification products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites; develop new products and services; conduct educational research; and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by Adobe Press. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.pearsonitcertification.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020