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Power Management and Performance in VMware vSphere 5.1 and 5.5

Power consumption is an important part of the datacenter cost strategy. Physical servers frequently offer a power management scheme that puts processors into low power states when not fully utilized, and VMware vSphere also offers power management techniques. A recent technical white paper describes the testing and results of two performance studies: The first shows how power management in VMware vSphere 5.5 in balanced mode (the default) performs 18% better than the physical host’s balanced mode power management setting. The second study compares vSphere 5.1 performance and power savings in two server models that have different generations of processors. Results show the newer servers have 120% greater performance and 24% improved energy efficiency over the previous generation.

For more information, please read the paper: Power Management and Performance in VMware vSphere 5.1 and 5.5.

VDI Performance Benchmarking on VMware Virtual SAN 5.5

In the previous blog series, we presented the VDI performance benchmarking results with VMware Virtual SAN public beta and now we announced the general availability of VMware Virtual SAN 5.5 which is part of VMware vSphere 5.5 U1 GA and VMware Horizon View 5.3.1 which supports Virtual SAN 5.5. In this blog, we present the VDI performance benchmarking results with the Virtual SAN GA bits and highlight the CPU improvements and 16-node scaling results. With Virtual SAN 5.5 with default policy, we could successfully run 1615 heavy VDI users (VDImark) out-of-the-box on a 16-node Virtual SAN cluster and see about 5% more consolidation when compared to Virtual SAN public beta.

virtualsan-view-block-diagram

To simulate the VDI workload, which is typically CPU bound and sensitive to I/O, we use VMware View Planner 3.0.1. We run View Planner and consolidate as many heavy users as we can on a particular cluster configuration while meeting the quality of service (QoS) criteria and we define the score as VDImark. For QoS criteria, View Planner operations are divided into three main groups: (1) Group A for interactive operations, (2) Group B for I/O operations, and (3) Group C for background operations. The score is determined separately for Group A user operations and Group B user operations by calculating the 95th percentile latency of all the operations in a group. The default thresholds are 1.0 second for Group A and 6.0 seconds for Group B. Please refer to the user guide, and the run and reporting guides for more details. The scoring is based on several factors such as the response time of the operations, compliance of the setup and configurations, and other factors.

As discussed in the previous blog, we used the same experimental setup (shown below) where each Virtual SAN host has two disk groups and each disk group has one PCI-e solid-state drive (SSD) of 200GB and six 300GB 15k RPM SAS disks. We use default policy when provisioning the automated linked clones pool with VMware Horizon View for all our experiments.

virtualsan55-setup

CPU Improvements in Virtual SAN 5.5

There were several optimizations done in Virtual SAN 5.5 compared to the previously available public beta version and one of the prominent improvements is the reduction of CPU usage for Virtual SAN. To highlight the CPU improvements, we compare the View Planner score on Virtual SAN 5.5 (vSphere 5.5 U1) and Virtual SAN public beta (vSphere 5.5).  On a 3-node cluster, VDImark (the maximum number of desktop VMs that can run with passing QoS criteria) is obtained for both Virtual SAN 5.5 and Virtual SAN public beta and the results are shown below:

virtualsan55-3node

The results show that with Virtual SAN 5.5, we can scale up to 305 VMs on a 3-node cluster, which is about 5% more consolidation when compared with Virtual SAN public beta. This clearly highlights the new CPU improvements in Virtual SAN 5.5 as a higher number of desktop VMs can be consolidated on each host with a similar user experience.

Linear Scaling in VDI Performance

In the next set of experiments, we continually increase the number of nodes for the Virtual SAN cluster to see how well the VDI performance scales. We collect the VDImark score on 3-node, 5-node, 8-node, 16-node increments, and the result is shown in the chart below.

virtualsan55-scaling

The chart illustrates that there is a linear scaling in the VDImark as we increase the number of nodes for the Virtual SAN cluster. This indicates good performance as the nodes are scaled up. As more nodes are added to the cluster, the number of heavy users that can be added to the workload increases proportionately. In Virtual SAN public beta, a workload of 95 heavy VDI users per host was achieved and now, due to CPU improvements in Virtual SAN 5.5, we are able to achieve 101 to 102 heavy VDI users per host. On a 16-node cluster, a VDImark of 1615 was achieved which accounts for about 101 heavy VDI users per node.

To further illustrate the Group A and Group B response times, we show the average response time of individual operations for these runs for both Group A and Group B, as follows.

virtualsan55-groupA

As seen in the figure above, the average response times of the most interactive operations are less than one second, which is needed to provide a good end-user experience. If we look all the way up to 16 nodes, we don’t see much variance in the response times, and they almost remain constant when scaling up. This clearly illustrates that, as we scale the number of VMs in larger nodes of a Virtual SAN cluster, the user experience doesn’t degrade and scales nicely.

virtualsan55-groupB

Group B is more sensitive to I/O and CPU usage than Group A, so the resulting response times are more important. The above figure shows how VDI performance scales in Virtual SAN. It is evident from the chart that there is not much difference in the response times as the number of VMs are increased from 305 VMs on a 3-node cluster to 1615 VMs on a 16-node cluster. Hence, storage-sensitive VDI operations also scale well as we scale the Virtual SAN nodes from 3 to 16.

To summarize, the test results in this blog show:

  • 5% more VMs can be consolidated on a 3-node Virtual SAN cluster
  • When adding more nodes to the Virtual SAN cluster, the number of heavy users supported increases proportionately (linear scaling)
  • The response times of common user operations (such as opening and saving files, watching a video, and browsing the Web) remain fairly constant as more nodes with more VMs are added.

To see the previous blogs on the VDI benchmarking with Virtual SAN public beta, check the links below:

VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 3

In part 1 and part 2 of the VDI/VSAN benchmarking blog series, we presented the VDI benchmark results on VSAN for 3-node, 5-node, 7-node, and 8-node cluster configurations. In this blog, we compare the VDI benchmarking performance of VSAN with an all flash storage array. The intent of this experiment is not to compare the maximum IOPS that you can achieve on these storage solutions; instead, we show how VSAN scales as we add more heavy VDI users. We found that VSAN can support a similar number of users as that of an all flash array even though VSAN is using host resources.

The characteristic of VDI workload is that they are CPU bound, but sensitive to I/O which makes View Planner a natural fit for this comparative study. We use VMware View Planner 3.0 for both VSAN and all flash SAN and consolidate as many heavy users as much we can on a particular cluster configuration while meeting the quality of service (QoS) criteria. Then, we find the difference in the number of users we can support before we run out of CPU, because I/O is not a bottleneck here. Since VSAN runs in the kernel and uses CPU on the host for its operation, we find that the CPU usage is quite minimal, and we see no more than a 5% consolidation difference for a heavy user run on VSAN compared to the all flash array.

As discussed in the previous blog, we used the same experimental setup where each VSAN host has two disk groups and each disk group has one PCI-e solid-state drive (SSD) of 200GB and six 300GB 15k RPM SAS disks. We built a 7-node and a 8-node cluster and ran View Planner to get the VDImark™ score for both VSAN and the all flash array. VDImark signifies the number of heavy users you can successfully run and meet the QoS criteria for a system under test. The VDImark for both VSAN and all flash array is shown in the following figure.

View Planner QoS (VDImark)

 

 From the above chart, we see that VSAN can consolidate 677 heavy users (VDImark) for 7-node and 767 heavy users for 8-node cluster. When compared to the all flash array, we don’t see more than 5% difference in the user consolidation. To further illustrate the Group-A and Group-B response times, we show the average response time of individual operations for these runs for both Group-A and Group-B, as follows.

Group-A Response Times

As seen in the figure above for both VSAN and the all flash array, the average response times of the most interactive operations are less than one second, which is needed to provide a good end-user experience.  Similar to the user consolidation, the response time of Group-A operations in VSAN is similar to what we saw with the all flash array.

Group-B Response Times

Group-B operations are sensitive to both CPU and IO and 95% should be less than six seconds to meet the QoS criteria. From the above figure, we see that the average response time for most of the operations is within the threshold and we see similar response time in VSAN when compared to the all flash array.

To see other parts on the VDI/VSAN benchmarking blog series, check the links below:
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 1
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 2
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 3

 

VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 2

In part 1, we presented the VDI benchmark results on VSAN for 3-node and 7-node configurations. In this blog, we update the results for 5-node and 8-node VSAN configurations and show how VSAN scales for these configurations.

The View Planner benchmark was run again to find the VDImark for different numbers of nodes (5 and 8 nodes) in a VSAN cluster as described in the previous blog and the results are shown in the following figure.

View Planner QoS (VDImark)

 

In the 5-node cluster, a VDImark score of 473 was achieved and for the 8-node cluster, a VDImark score of 767 was achieved. These results are similar to the ones we saw on the 3-node and 7-node cluster earlier (about 95 VMs per host). So, there is nice scaling in terms of maximum VMs supported as the numbers of nodes were increased in the VSAN from 3 to 8.

To further illustrate the Group-A and Group-B response times, we show the average response time of individual operations for these runs for both Group-A and Group-B, as follows.

Group-A Response Times

As seen in the figure above, the average response times of the most interactive operations are less than one second, which is needed to provide a good end-user experience. If we look at the new results for 5-node and 8-node VSAN, we see that for most of the operations, the response time mostly remains the same across different node configurations.

Group-B Response Times

Since Group-B is more sensitive to I/O and CPU usage, the above chart for Group-B operations is more important to see how View Planner scales. The chart shows that there is not much difference in the response times as the number of VMs were increased from 286 VMs on a 3-node cluster to 767 VMs on an 8-node cluster. Hence, storage-sensitive VDI operations also scale well as we scale the VSAN nodes from 3 to 8 and user experience expectations are met.

To see other parts on the VDI/VSAN benchmarking blog series, check the links below:
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 1
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 2
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 3

 

 

Each vSphere release introduces new vMotion functionality, increased reliability and significant performance improvements. vSphere 5.5 continues this trend by offering new enhancements to vMotion to support EMC VPLEX Metro, which enables shared data access across metro distances.

In this blog, we evaluate vMotion performance on a VMware vSphere 5.5 virtual infrastructure that was stretched across two geographically dispersed datacenters using EMC VPLEX Metro.

Test Configuration

The VPLEX Metro test bed consisted of two identical VPLEX clusters, each with the following hardware configuration:

• Dell R610 host, 8 cores, 48GB memory, Broadcom BCM5709 1GbE NIC
• A single engine (two directors) VPLEX Metro IP appliance
• FC storage switch
• VNX array, FC connectivity, VMFS 5 volume on a 15-disk RAID-5 LUN


Figure 1. Logical layout of the VPLEX Metro deployment

Figure 1 illustrates the deployment of the VPLEX Metro system used for vMotion testing. The figure shows two data centers, each with a vSphere host connected to a VPLEX Metro appliance. The VPLEX virtual volumes presented to the vSphere hosts in each data center are synchronous, distributed volumes that mirror data between the two VPLEX clusters using write-through caching. As a result, vMotion views the underlying storage as shared storage, or exactly equivalent to a SAN that both source and destination hosts have access to. Hence, vMotion in a Metro VPLEX environment is as easy as traditional vMotion that live migrates only the memory and device state of a virtual machine.

The two VPLEX Metro appliances in our test configuration used IP-based connectivity. The vMotion network between the two ESXi hosts used a physical network link distinct from the VPLEX network. The Round Trip Time (RTT) latency on both VPLEX and vMotion networks was 10 milliseconds.

Measuring vMotion Performance

The following metrics were used to understand the performance implications of vMotion:

• Migration Time: Total time taken for migration to complete
• Switch-over Time: Time during which the VM is quiesced to enable switchover from source to the destination host
• Guest Penalty: Performance impact on the applications running inside the VM during and after the migration

Test Results


Figure 2. VPLEX Metro vMotion performance in vSphere 5.1 and vSphere 5.5

Figure 2 compares VPLEX Metro vMotion performance results in vSphere 5.1 and vSphere 5.5 environments. The test scenario used an idle VM configured with 2 VCPUs and 2GB memory. The figure shows a minor difference in the total migration time between the two vSphere environments and a significant improvement in vMotion switch-over time in the vSphere 5.5 environment. The switch-over time reduced from about 1.1 seconds to about 0.6 second (a nearly 2x improvement), thanks to a number of performance enhancements that are included in the vSphere 5.5 release.

We also investigated the impact of VPLEX Metro live migration on Microsoft SQL Server online transaction processing (OLTP) performance using the open-source DVD Store workload. The test scenario used a Windows Server 2008 VM configured with 4 VCPUs, 8GB memory, and a SQL Server database size of 50GB.


Figure 3. VPLEX Metro vMotion impact on SQL Server Performance

Figure 3 plots the performance of a SQL Server virtual machine in orders processed per second at a given time—before, during, and after VPLEX Metro vMotion. As shown in the figure, the impact on SQL Server throughput was very minimal during vMotion. The SQL Server throughput on the destination host was around 310 orders per second, compared to the throughput of 350 orders per second on the source host. This throughput drop after vMotion is due to the VPLEX inter-cluster cache coherency interactions and is expected. For some time after the vMotion, the destination VPLEX cluster continued to send cache page queries to the source VPLEX cluster and this has some impact on performance. After all the metadata is fully migrated to the destination cluster, we observed the SQL Server throughput increase to 350 orders per second, the same level of throughput seen prior to vMotion.

These performance test results show the following:

  • Remarkable improvements in vSphere 5.5 towards reducing vMotion switch-over time during metro migrations (for example, a nearly 2x improvement over vSphere 5.1)
  • VMware vMotion in vSphere 5.5 paired with EMC VPLEX Metro can provide workload federation over a metro distance by enabling administrators to dynamically distribute and balance the workloads seamlessly across data centers

To find out more about the test configuration, performance results, and best practices to follow, see our recently published performance study.

SEsparse Shows Significant Improvements over VMFSsparse

Limited amounts of physical resources can make large-scale virtual infrastructure deployments challenging. Provisioning dedicated storage space to hundreds of virtual machines can become particularly expensive. To address this VMware vSphere 5.5 provides two sparse storage techniques, namely VMFSparse and SEsparse. Running multiple VMs using sparse delta-disks with a common parent virtual disk brings down the required amount of physical storage making large-scale deployments manageable. SEsparse was introduced in VMware vSphere 5.1 and in vSphere 5.5 became the default virtual disk snapshotting technique for VMDKs greater than 2 TB. Various enhancements were made to SEsparse technology in the vSphere 5.5 release, which makes SEsparse perform mostly on par or better than VMFSsparse formats. In addition dynamic space reclamation confers on SEsparse a significant advantage over VMFSsparse virtual disk formats. This feature makes SEsparse the choice for VMware® Horizon View™ environments where space reclamation is critical due to the large number of tenants sharing the underlying storage.


A recently published paper reports the results from a series of performance studies of SEsparse and VMFsparse using thin virtual disks as baselines. The performance was evaluated using a comprehensive set of Iometer workloads along with workloads from two real world application domains: Big Data Analytics and Virtual Desktop Infrastructure (VDI). Overall, the performance of SEsparse is significantly better than the VMFSsparse format for random write workloads and mostly on par or better for the other analyzed workloads, depending on type.

Read the full performance study, “SEsparse in VMware vSphere 5.5.”

VDI Benchmarking Using View Planner on VMware Virtual SAN (VSAN)

VMware vSphere® 5.5 introduces the beta availability of VMware® Virtual SAN (VSAN). This feature allows a new software-defined storage tier, pools compute and direct-attached storage resources, and clusters server disks and flash to create resilient shared storage.

This blog showcases Virtual Desktop Infrastructure (VDI) performance on Virtual SAN using VMware View Planner, which is designed to simulate a large-scale deployment of virtualized desktop systems. This is achieved by generating a workload representative of many user-initiated operations that take place in a typical VDI environment. The results allow us to study the effects on an entire virtualized infrastructure including the storage subsystem. View Planner can be downloaded here.

In this blog, we evaluate the performance of VSAN using View Planner with different VSAN node configurations. In this experiment, we build a 3-node VSAN cluster and a 7-node VSAN cluster to determine the maximum number of VDI virtual machines (VMs) we can run while meeting the quality of service (QoS) criteria set for View Planner.  The maximum number of passing VMs is called the VDImark™ for a given system under test. This metric is used for VDI benchmarking and it encapsulates the number of VDI users that can be run on a given system with an application response time less than the set threshold. For response time characterization, View Planner operations are divided into three main groups: (1) Group A for interactive operations, (2) Group B for I/O operations, and (3) Group C for background operations. The score is determined separately for Group A user operations and Group B user operations by calculating the 95th percentile latency of all the operations in a group. The default thresholds are 1.0 second for Group A and 6.0 seconds for Group B. Please refer to the user guide, and the run and reporting guides for more details. Hence, the scoring is based on several factors such as the response time of the operations, compliance of the setup and configurations, and so on.

Experimental Setup

The host running the desktop VMs has 16 Intel Xeon E5-2690 cores running @ 2.9GHz. The host has 256GB physical RAM, which is more than sufficient to run 100 1GB Windows 7 VMs. For VSAN, each host has two disk groups where each disk group has one PCI-e solid-state drive (SSD) of 200GB and six 300GB 15k RPM SAS disks.

View Planner QoS (VDImark)

The View Planner benchmark was run to find the VDImark for both 3-node and 7-node VSAN clusters and the results are shown in the chart above. In the 3-node cluster, a VDImark of 286 was achieved and for 7-node cluster, a VDImark score of 677 was achieved. So, there is nice scaling in terms of maximum VMs supported as the numbers of nodes were increased in VSAN from 3 to 7.

To further illustrate the Group A and Group B response times, we show the average response time of individual operations for these runs for both Group A and Group B, as follows.

Group A Response Times

As seen in the figure above, the average response times of the most interactive operations are less than one second, which is needed to provide good end-user experience. If we look at the 3-node and 7-node run, we don’t see much variance in the response times, and they almost remain constant when scaling up. This clearly illustrates that, as we scale the number of VMs in larger nodes of a VSAN cluster, the user experience doesn’t degrade and scales nicely.

Group B Response Times

Since Group B is more sensitive to I/O and CPU usage, the above chart for Group B operations is more important to see how we scale. It is evident from the chart that there is not much difference in the response times as the number of VMs were increased from 286 VMs on a 3-node cluster to 677 VMs on a 7-node cluster. Hence, storage-sensitive VDI operations also scale well as we scale the VSAN nodes from 3 to 7.

To see other parts on the VDI/VSAN benchmarking blog series, check the links below:
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 1
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 2
VDI Benchmarking Using View Planner on VMware Virtual SAN – Part 3

vSphere Flash Read Cache Performance on vSphere 5.5

vSphere Flash Read Cache (vFRC) is a new solution to enhance storage I/O performance in vSphere 5.5.  vFRC lets you use flash devices (SSD or PCIe cards) as a read cache for VM I/Os and therefore improve performance. vFRC can improve performance for read-intensive workloads that have a high percentage of data locality. Such workloads are generated by database warehousing applications and enterprise server applications such as Web proxy servers and monitoring servers.

A recent technical white paper studies the performance of vFRC with respect to the following workloads:

  • A decision support system (DSS) database workload running with Oracle 11g R2
  • A DVD store workload running with Microsoft SQL Server 2008
  • Enterprise server-level I/O traces that are used extensively in storage research

The results are presented in the paper, along with performance best practices when using vFRC. The paper also gives an overview of the vFRC architecture. To learn more, read Performance of vSphere Flash Read Cache in VMware vSphere 5.5.

VMware vFabric Postgres 9.2 Performance and Best Practices

VMware vFabric Postgres (vPostgres) 9.2 improves vertical scalability over the previous version by 300% for pgbench SELECT-only (a common read-only OLTP workload) and by 100% for pgbench (a common read/write OLTP workload). vPostgres 9.2 on vSphere 5.1 achieves equal-to-native vertical scalability on a 32-core machine.

Using out-of-the-box settings for both vPostgres and vSphere, virtual machine (VM)-based database consolidation performs on par with alternative approaches (such as consolidated on one vPostgres server instance or consolidated on multiple vPostgres server instances but one operating system instance) in a baseline memory-undercommitted situation for a standard OLTP workload (using dbt2 benchmark, an open-source fair implementation of TPC-C); while performs increasingly more robust as memory overcommitment escalates (200% better than alternatives under a 55% memory-overcommitted situation).

By using an unconventionally larger database shared buffers (75% of memory size rather than the conventional 25%), vPostgres can attain both better performance (12% better) and more consistent performance (70% less temporal variation).

When using an unconventionally larger database shared buffers, the vPostgres database memory ballooning technique can enhance the robustness of VM-based database consolidation: under a 55% memory-overcommitted situation, using its help can advance the performance advantage of VM-based consolidation over alternatives from 60% to 140%.

For more details including experimentation methodology and references, please read the namesake whitepaper.

Performance Best Practices for vSphere 5.5 is Available

We are pleased to announce the availability of Performance Best Practices for vSphere 5.5. This is a book designed to help system administrators obtain the best performance from vSphere 5.5 deployments.

The book addresses many of the new features in vSphere 5.5 from a performance perspective. These include:

  • vSphere Flash Read Cache, a new feature in vSphere 5.5 allowing flash storage resources on the ESXi host to be used for read caching of virtual machine I/O requests.
  • VMware Virtual SAN (VSAN), a new feature (in beta for vSphere 5.5) allowing storage resources attached directly to ESXi hosts to be used for distributed storage and accessed by multiple ESXi hosts.
  • The VMware vFabric Postgres database (vPostgres).

We’ve also updated and expanded on many of the topics in the book. These include:

  • Running storage latency and network latency sensitive applications
  • NUMA and Virtual NUMA (vNUMA)
  • Memory overcommit techniques
  • Large memory pages
  • Receive-side scaling (RSS), both in guests and on 10 Gigabit Ethernet cards
  • VMware vMotion, Storage vMotion, and Cross-host Storage vMotion
  • VMware Distributed Resource Scheduler (DRS) and Distributed Power Management (DPM)
  • VMware Single Sign-On Server

The book can be found here.