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Scaling Performance for VAIO in vSphere 6.0 U1

by Chien-Chia Chen

vSphere APIs for I/O Filtering (VAIO) is a framework that enables third-party software developers to implement data services, such as caching and replication, to vSphere. Figure 1 below shows the general architecture of VAIO. Once I/O filter libraries are installed to a virtual disk (VMDK), every I/O request generated from the guest operating system to the VMDK will first be intercepted by the VAIO framework at the file device layer. The VAIO framework then hands over the I/O request to the user space I/O filter libraries, where a series of third party data service operations can be performed against the I/O. After processing the I/O, user space I/O filter libraries return the I/O back to the VAIO framework, which continues the rest of the issuing path. Similarly, upon completion, the I/O will first be processed by the user space I/O filter libraries before continuing its original completion path.

There have been questions around the overhead of the VAIO framework due to its extra user-to-kernel communication. In this blog post, we evaluate the performance of vSphere APIs for I/O Filtering using a null I/O filter and demonstrate how VAIO scales with respect to the number of virtual machines and outstanding I/Os (OIOs). The null I/O filter accepts each I/O request and immediately returns it.


Figure 1. vSphere APIs for I/O Filtering Architecture

System Configuration

The configuration of our systems is as follows:

  • One ESXi host
    • Machine: Dell R720 server running vSphere 6.0 Update 1
    • CPU: 16-core, 2-socket (32 hyper-threads) Intel® Xeon® E5-2665 @ 2.4 GHz
    • Memory: 128GB memory
    • Physical Disk: One Intel® S3700 400GB SATA SSD on LSI MegaRAID SAS controller
    • VM: Up to 32 link-cloned I/O Analyzer 1.6.2 VMs (SUSE Linux Enterprise 11 SP2; 1 virtual CPU (VCPU) and 1GB memory each). Each virtual machine has 1 PVSCSI controller hosting two 1GB VMDKs—one has no I/O filter and another has a null filter, both think-provisioned.
  • Workload: Iometer 4K sequential read (4K-aligned) with various number of OIOs


We conduct two sets of tests separately—one against VMDK without an I/O filter (referred to as “default”) and another against the null-filter VMDK (referred to as “iofilter”). In each set of tests, every virtual machine has one Iometer disk worker to generate 4K sequential read I/Os to the VMDK under test. We have a 2-minute warm-up time and measure I/Os per second (IOPS), normalized CPU cost, and read latency over the next 2-minute test duration. The latency is the median of the average read latencies reported by all Iometer workers.

Note that I/O sizes and access patterns do not affect the performance of VAIO since it does no additional data copying, maintains the original access patterns, and incurs no extra access to physical disks.


VM Scaling

Figures 2 and 3 below show the IOPS, CPU cost per 1K IOPS, and latency with a different number of virtual machines at 128 OIOs. Except for the single virtual machine test, results show that VAIO achieves similar IOPS and has similar latency compared to the default VMDK. However, VAIO introduces 10%-20% higher CPU overhead per 1K IOPS. The single virtual machine IOPS with iofilter is 80% higher than the default VMDK. This is because, in the default case, the VCPU performs the majority of synchronous I/O work; whereas, in the iofilter case, VAIO contexts take over a big portion of the work and unblock the VCPU from generating more I/Os. With additional VCPUs and Iometer disk workers to mitigate the single core bottleneck, the default VMDK is also able to drive over 70K IOPS.

Figure 2. IOPS and CPU Cost vs. Number of VMs (128 Outstanding I/Os)


Figure 3. Iometer Read Latency vs. Number of VMs (128 Outstanding I/Os)


OIO Scaling

Figures 4 and 5 below show the IOPS, CPU cost per 1K IOPS, and latency with a different number of OIOs at 16 virtual machines. A similar trend again holds that VAIO achieves the same IOPS and has the same latency compared to the default VMDK while it incurs 10%-20% higher CPU overhead per 1K IOPS.


Figure 4. Percent of a Core per 1 Thousand IOPS vs. Outstanding I/Os (16 VMs)


Figure 5. Iometer Read Latency vs. Outstanding I/Os (16 VMs)


Based on our evaluation, VAIO achieves comparable throughput and latency performance at a cost of 10%-20% more CPU cycles. From our experience, when using the VAIO framework, we recommend the following general best practices:

  • Reduce CPU over-commitment. The VAIO framework introduces at least one additional context per VMDK with an active filter. Over-committing CPU can result in intensive CPU contention, thus much worse virtualization efficiency.
  • Avoid blocking when developing I/O filter libraries. Keep in mind that an I/O will be blocked until the user space I/O filter finishes processing. Thus additional processing time will result in higher end-to-end latency.
  • Increase concurrency wisely when developing I/O filter libraries. The user space I/O filter can potentially serve I/Os from all VMDKs. Thus, when developing I/O filter libraries, it is important to be flexible in terms of concurrency to avoid a single core CPU bottleneck and meanwhile without introducing too many unnecessary active contexts that cause higher CPU contention.


Dynamic Host-Wide Performance Tuning in VMware vSphere 6.0

by Chien-Chia Chen


The networking stack of vSphere is, by default, tuned to balance the tradeoffs between CPU cost and latency to provide good performance across a wide variety of applications. However, there are some cases where using a tunable provides better performance. An example is Web-farm workloads, or any circumstance where a high consolidation ratio (lots of VMs on a single ESXi host) is preferred over extremely low end-to-end latency. VMware vSphere 6.0 introduces the Dynamic Host-Wide Performance Tuning  feature (also known as dense mode), which provides a single configuration option to dynamically optimize individual ESXi hosts for high consolidation scenarios under certain use cases. Later in this blog, we define those use cases. Right now, we take a look at how dense mode works from an internal viewpoint.

Mitigating Virtualization Inefficiency under High Consolidation Scenarios

Figure 1 shows an example of the thread contexts within a high consolidation environment. In addition to the Virtual CPUs (each labeled VCPU) of the VMs, there are per-VM vmkernel threads (device-emulation, labeled “Dev Emu”, threads in the figure) and multiple vmkernel threads for each Physical NIC (PNIC) executing physical device virtualization code and virtual switching code. One major source of virtualization inefficiency is the frequent context switches among all these threads. While context switches occur due to a variety of reasons, the predominant networking-related reason is Virtual NIC (VNIC) Interrupt Coalescing, namely, how frequently does the vmkernel interrupt the guest for new receive packets (or vice versa for transmit packets). More frequent interruptions are likely to result in lower per-packet latency while increasing virtualization overhead. At very high consolidation ratios, the overhead from increased interrupts hurts performance.

Dense mode uses two techniques to reduce the number of context switches:

  • The VNIC coalescing scheme will be changed to a less aggressive scheme called static coalescing.
    With static coalescing, a fixed number of requests are delivered in each batch of communication between the Virtual Machine Monitor (VMM) and vmkernel. This, in general, reduces the frequency of communication, thus fewer context switches, resulting in better virtualization efficiency.
  • The device emulation vmkernel thread wakeup opportunities are greatly reduced.
    The device-emulation threads now will only be executed either periodically with a longer timer or when the corresponding VCPUs are halted. This optimization largely reduces the frequency that device emulation threads being waken up, so frequency of context switch is also lowered.


Figure 1. High Consolidation Example

Enabling Dense Mode

Dense mode is disabled by default in vSphere 6.0. To enable it, change Net.NetTuneHostMode in the ESXi host’s Advanced System Settings (shown below in Figure 2) to dense.


Figure 2. Enabling Dynamic Host-Wide Performance Tuning
“default” is disabled; “dense” is enabled

Once dense mode is enabled, the system periodically checks the load of the ESXi host (every 60 seconds by default) based on the following three thresholds:

  • Number of VMs ≥ number of PCPUs
  • Number of VCPUs ≥ number of 2 * PCPUs
  • Total PCPU utilization ≥ 50%

When the system load exceeds the above thresholds, these optimizations will be in effect for all regular VMs that carry default settings. When the system load drops below any of the thresholds, those optimizations will be automatically removed from all affected VMs such that the ESXi host performs identical to when dense mode is disabled.

Applicable Workloads

Enabling dense mode can potentially impact performance negatively for some applications. So, before enabling, carefully profile the applications to determine whether or not the workload will benefit from this feature. Generally speaking, the feature improves the VM consolidation ratio on an ESXi host running medium network throughput applications with some latency tolerance and is CPU bounded. A good use case is Web-farm workload, which needs CPU to process Web requests while only generating a medium level of network traffic and having a few milliseconds of tolerance to end-to-end latency. In contrast, if the bottleneck is not at CPU, enabling this feature results in hurting network latency only due to less frequent context switching. For example, the following workloads are NOT good use cases of the feature:

  • X Throughput-intensive workload: Since network is the bottleneck, reducing the CPU cost would not necessarily improve network throughput.
  • X Little or no network traffic: If there is too little network traffic, all the dense mode optimizations barely have any effect.
  • X Latency-sensitive workload: When running latency-sensitive workloads, another set of optimizations is needed and is documented in the “Deploying Extremely Latency-Sensitive Applications in VMware vSphere 5.5” performance white paper.


To evaluate this feature, we implement a lightweight Web benchmark, which has two lightweight clients and a large number of lightweight Web server VMs. The clients send HTTP requests to all Web servers at a given request rate, wait for responses, and report the response time. The request is for static content and it includes multiple text and JPEG files totaling around 100KB in size. The Web server has memory caching enabled and therefore serves all the content from memory. Two different request rates are used in the evaluation:

  1. Medium request rate: 25 requests per second per server
  2. High request rate: 50 requests per second per server

In both cases, the total packet rate on the ESXi host is around 400 Kilo-Packets/Second (KPPS) to 700 KPPS in each direction, where the receiving packet rate is slightly higher than the transmitting packet rate.

System Configuration

We configured our systems as follows:

  • One ESXi host (running Web server VMs)
    • Machine: HP DL580 G7 server running vSphere 6.0
    • CPU: Four 10-core Intel® Xeon® E7-4870 @ 2.4 GHz
    • Memory: 512 GB memory
    • Physical NIC: Two dual-port Intel X520 with a total of three active 10GbE ports
    • Virtual Switching: One virtual distributed switch (vDS) with three 10GbE uplinks using default teaming policy
    • VM: Red Hat Linux Enterprise Server 6.3 assigned one VCPU, 1GB memory, and one VMXNET3 VNIC
  • Two Clients (generating Web requests)
    • Machine: HP DL585 G7 server running Red Hat Linux Enterprise Server 6.3
    • CPU: Four 8-core AMD Opteron™ 6212 @ 2.6 GHz
    • Memory: 128 GB memory
    • Physical NIC: One dual-port Intel X520 with one active 10GbE port on each client


Medium Request Rate

We first present the evaluation results for medium request rate workloads. Figures 3 and 4 below show the 95th-percentile response time and total host CPU utilization as the number of VMs increase, respectively. For the 95th-percentile response time, we consider 100ms as the preferred latency tolerance.

Figure 3 shows that at 100ms, default mode consolidates only about 470 Web server VMs, whereas dense mode consolidates more than 510 VMs, which is an over 10% improvement. For CPU utilization, we consider 90% is the desired maximum utilization.


Figure 3. Medium Request Rate 95-Percentile Response Time
(Latency Tolerance 100ms)

Figure 4 shows that at 90% utilization, default mode consolidates around 465 Web server VMs, whereas dense mode consolidates about 495 Web server VMs, which is still a nearly 10% improvement in consolidation ratio. We also notice that dense mode, in fact, also reduces response time. This is because the great reduction in context switching improves virtualization efficiency, which compensates the increase in latency due to more aggressive batching.


Figure 4. Medium Request Rate Host Utilization
(Desired Maximum Utilization 90%)

High Request Rate

Figures 5 and 6 below show the 95th-percentile response time and total host CPU utilization for a high request rate as the number of VMs increase, respectively. Because the request rate is doubled, we reduce the number of Web server VMs consolidated on the ESXi host. Figure 5 first shows that at 100ms response time, dense mode only consolidates about 5% more VMs in a medium request rate case (from ~280 VMs to ~290 VMs). However, if we look at the CPU utilization as shown in Figure 6, at 90% desired maximum load, dense mode still consolidates about 10% more VMs (from ~ 240 VMs to ~260 VMs). Considering both response time and utilization metrics, because there are a fewer number of active contexts under the high request rate workload, the benefit of reducing context switches will be less significant compared to a medium request rate case.


Figure 5. High Request Rate 95-Percentile Response Time
(Latency Tolerance 100ms)


Figure 6. High Request Rate Host Utilization
(Desired Maximum Utilization at 90%)


We presented the Dynamic Host-Wide Performance Tuning feature, also known as dense mode. We proved a Web-farm-like workload achieves up to 10% higher consolidation ratio while still meeting 100ms latency tolerance and 90% maximum host utilization. We emphasized that the improvements do not apply to every kind of application. Because of this, you should carefully profile the workloads before enabling dense mode.

VMware Virtual SAN Stretched Cluster Best Practices White Paper

VMware Virtual SAN 6.1 introduced the concept of a stretched cluster which allows the Virtual SAN customer to configure two geographically located sites, while synchronously replicating data between the two sites. A technical white paper about the Virtual SAN stretched cluster performance has now been published. This paper provides guidelines on how to get the best performance for applications deployed on a Virtual SAN stretched cluster environment.

The chart below, borrowed from the white paper, compares the performance of the Virtual SAN 6.1 stretched cluster deployment against the regular Virtual SAN cluster without any fault domains. A nine- node Virtual SAN stretched cluster is considered with two different configurations of inter-site latency: 1ms and 5ms. The DVD Store benchmark is executed on four virtual machines on each host of the nine-node Virtual SAN stretched cluster. The DVD Store performance metrics of cumulated orders per minute in the cluster, read/write IOPs, and average latency are compared with a similar workload on the regular Virtual SAN cluster. The orders per minute (OPM) is lower by 3% and 6% for the 1ms and 5ms inter-site latency stretched cluster compared to the regular Virtual SAN cluster.

Figure 1a.  DVD Store orders per minute in the cluster and guest IOPS comparison

Guest read/write IOPS and latency were also monitored. The read/write mix ratio for the DVD Store workload is roughly at 1/3 read and 2/3 write. Write latency shows an obvious increase trend when the inter-site latency is higher, while the read latency is only marginally impacted. As a result, the average latency increases from 2.4ms to 2.7ms, and 5.1ms for 1ms and 5ms inter-site latency configuration.

Figure 1b.  DVD Store latency comparison

These results demonstrate that the inter-site latency in a Virtual SAN stretched cluster deployment has a marginal performance impact on a commercial workload like DVD Store. More results are available in the white paper.

Measuring Cloud Scalability Using the Zephyr Benchmark

Cloud-based deployments continue to be a hot topic in many of today’s corporations.  Often the discussion revolves around workload portability, ease of migration, and service pricing differences.  In an effort to bring performance into the discussion we decided to leverage VMware’s new benchmark, Zephyr.  As a follow-on to Harold Rosenberg’s introductory Zephyr post we decided to showcase some of the flexibility and scalability of our new large-scale benchmark.  Previously, Harold presented some initial scalability data running on three local vSphere 6 hosts.  For this article, we decided to extend this further by demonstrating Zephyr’s ability to run within a non-VMware cloud environment and scaling up the number of app servers.

Zephyr is a new web-application benchmark architected to simulate modern-day web applications.  It consists of a benchmark application and a workload driver.  Combined, they simulate the behavior of everyday users attending a real-time auction.  For more details on Zephyr I encourage you to review the introductory post.

Environment Configuration:
Cloud Environment: Amazon AWS, US West.
Instance Types: M3.XLarge, M3.Large, C3.Large.
Instance Notes: Database instances utilized an additional 300GB io1 tier data disk.
Instance Operating System: Centos 6.5 x64.
Application: Zephyr Internal Build 084.

Testing Methodology:
All instances were run within the same cloud environment to reduce network-induced latencies.  We started with a base configuration consisting of eight instances.  We then  scaled out the number of workload drivers and application servers in an effort to identify how a cloud environment scaled as application workload needs increased.  We used Zephyr’s FindMax functionality which runs a series of tests to determine the maximum number of users the configuration can sustain while still meeting QoS requirements.  It should be noted that the early experimentation allowed us to identify the maximum needs for the other services beyond the workload drivers and application servers to reduce the likelihood of bottlenecks in these services.  Below is a block diagram of the configurations used for the scaled-out Zephyr deployment.


For our analysis of Zephyr cloud scaling we ran multiple iterations for each scale load level and selected the average.  We automated the process to ensure consistency.  Our results show both the number of users sustained as well as the http requests per second as reported by the benchmark harness.


As you can see in the above graph, for our cloud environment running Zephyr, scaling the number of applications servers yielded nearly linear scaling up to five application servers. The delta in scaling between the number of users and the http requests per second sustained was less than 1%.  Due to time constraints we were unable to test beyond five application servers but we expect that the scaling would have continued upwards well beyond the load levels presented.

Although just a small sample of what Zephyr and cloud environments can scale to, this brief article highlights both the benchmark and cloud environment scaling.  Though Zephyr hasn’t been released publically yet, it’s easy to see how this type of controlled, scalable benchmark will assist in performance evaluations of a diverse set of environments.  Look for more Zephyr based cloud performance analysis in the future.

Large Receive Offload (LRO) Support for VMXNET3 Adapters with Windows VMs on vSphere 6

Large Receive Offload (LRO) is a technique to reduce the CPU time for processing TCP packets that arrive from the network at a high rate. LRO reassembles incoming packets into larger ones (but fewer packets) to deliver them to the network stack of the system. LRO processes fewer packets, which reduces its CPU time for networking. Throughput can be improved accordingly since more CPU is available to deliver additional traffic. On Windows, LRO is also referred to as Receive Segment Coalescing (RSC).

LRO has been supported for Linux VMs with kernel 2.6.24 and later since vSphere 4. With the introduction of Windows Server 2012 and Windows 8 supporting LRO, vSphere 6 now adds support for LRO on a VMXNET3 adapter on Windows VMs. LRO is especially beneficial in the virtualized environment in which resources are shared by multiple VMs. This blog shows the performance benefits of using LRO for Windows VMs running on vSphere 6.

Test-Bed Setup

The test bed consists of a vSphere 6.0 host running VMs and a client machine that drives workload generation. Both machines have dual-socket, 6-core 2.9GHz Intel Xeon E5-2667 (Sandy Bridge) processors. The client machine is configured with native Red Hat Enterprise Linux 6 that generates TCP flows. The VMs in the vSphere host run Windows 2012 Server and are configured with 4 vCPUs and 2GB RAM. Both machines have an Intel 82599EB 10Gbps adapter installed, which are connected using a 10 Gigabit Ethernet (GbE) switch.

Performance Results

1. Native to VM Traffic

Figures 1 and 2 show a CPU efficiency and throughput comparison with and without LRO when TCP streams are generated from the client machine to a VM running on the vSphere host. Netperf is used to generate traffic. Three different message sizes are used: 256 bytes, 16KB, 64KB. The socket size is set to 8K, 64K, and 256K respectively. The message size is used to determine the number of bytes that Netperf delivers to the TCP stack in the client machine, which then determines the actual packet sizes. The NIC in the client machine splits packets with a size larger than the MTU (1500 is used for this blog) into smaller MTU-sized ones before sending them out. Once packets are received in the vSphere host, LRO aggregates packets and delivers larger packets (but smaller in number) to the receiving VM running in the host. This process is done in either hardware (that is, the physical NIC) or software (that is, the vSphere networking stack) depending on the NIC type and configuration. In this blog, LRO is performed in the vSphere networking stack before packets are delivered to the VM.

CPU efficiency is calculated by dividing the throughput by the number of CPU cores used for both the hypervisor and the VM, representing how much throughput in gigabits per second (Gbps) a single CPU core can receive. For example, a CPU efficiency of 5 means the system can handle 5Gbps with one core. Therefore, a higher CPU efficiency is desirable.

As shown in Figures 1 and 2, using LRO considerably improves both CPU efficiency and throughput with all three message sizes. Figure 1 shows that CPU efficiency improves by 86%, 25%, and 33% with 256 byte, 16KB, and 64KB messages respectively, when compared to the case without LRO. Figure 2 shows throughput improves 54% for 256 byte messages (0.6Gbps to 0.9Gbps) and 5% for 16KB messages (9.0Gbps to 9.4Gbps). There is not much difference for 64KB packets (9.5Gbps to 9.4Gbps, this is within a rage of normal variance). With 16KB and 64KB messages, throughput with LRO is already at line rate (that is, 10Gbps), which is why the improvement is not as significant as CPU efficiency.

Figure 3 compares the packet rate and the average packet size between the messages with LRO and without LRO. They are measured right before packets are delivered to the VM (but after LRO is performed for the LRO configuration). It is clearly shown that fewer but larger packets are delivered to the VM with LRO. For example, with 64KB messages, the packet rate delivered to the VM decreases from 815K packets per second (pps) to 113Kpps with LRO, while the packet size increases from 1.5KB to 10.5KB. The number of interrupts generated for the guest also becomes smaller accordingly, helping to improve overall CPU efficiency and throughput.

When the client machine generates packets, those with a size larger than the MTU are split into smaller MTU-sized packets before being sent out. With a larger message size, more MTU-sized packets are produced and the packet rate received by the vSphere host increases accordingly. This is why the packet rate becomes higher for 16KB and 64KB messages than 256 byte messages without LRO in Figure 3. LRO aggregates the received packets before they hit the VM so the packet rate remains low regardless of the message size in the figure.

Figure 1

Figure 1. CPU efficiency comparison with and without LRO with Native-VM traffic

Figure 2. Throughput comparison with and without LRO with Native-VM traffic

Figure 2. Throughput comparison with and without LRO with Native-VM traffic

Figure 3. Packet rate and size comparison with and without LRO with Native-VM traffic, when packets are delivered to the VM

Figure 3. Packet rate and size comparison with and without LRO with Native-VM traffic, when packets are delivered to the VM

2. VM to VM Local Traffic

LRO is also beneficial in VM-VM local traffic where VMs are located in the same host, communicating with each other through a virtual switch. Figures 4 and 5 depict CPU efficiency and throughput comparisons with and without LRO with two VMs on the same host sending and receiving TCP flows. The same message and socket sizes as the Native-VM tests above are used.

From Figure 4, LRO improves CPU efficiency by 15%, 92%, and 90% with 256 bytes, 16KB, and 64KB byte messages respectively, when compared to the case without LRO. Figure 5 shows throughput also improves by 20% with 256 byte messages (0.8Gbps to 1.0Gbps), by 103% with 16KB messages (9.0Gbps to 18.4Gbps), and by 142% with 64KB (11.7Gbps to 28.4Gbps).

Without LRO, big packets with a size larger than the MTU need to be split before delivered to the receiving VM, similar to Native-VM traffic. This is because the receiver cannot handle those big packets. LRO saves the time spent in both splitting packets and receiving smaller packets since packet splitting also happens on the vSphere host with VM-VM Local traffic. This explains why the improvement with 16KB and 64KB messages is higher than that of Native-VM traffic. The absolute CPU efficiency in VM-VM local traffic can become lower than that in Native-VM traffic since the CPU time of both sending and receiving VMs are included for this calculation.

As expected, the packet rate decreases while the average packet size increases with LRO as shown in Figure 6. For example, with 64KB messages, the packet rate delivered to the VM becomes reduced from 1009Kpps to 240Kpps, while the packet size gets increased from 1.6KB to 14.9KB.

The packet rate becomes higher for 256 byte messages with LRO, most likely because round-trip time (RTT) gets reduced due to the use of LRO. With the average packet size being similar to each other between LRO and without LRO, this effectively helps to improve throughput and correspondingly CPU efficiency, as seen in Figure 4 and 5.

Figure 4. CPU efficiency with and without LRO with VM-VM Local traffic

Figure 4. CPU efficiency with and without LRO with VM-VM Local traffic

Figure 5. Throughput comparison with and without LRO with VM-VM Local traffic

Figure 5. Throughput comparison with and without LRO with VM-VM Local traffic

Figure 6. Packet rate and size comparison with and without LRO with VM-VM Local traffic, when packets are delivered to the VM

Figure 6. Packet rate and size comparison with and without LRO with VM-VM Local traffic, when packets are delivered to the VM

Enable or Disable LRO on a VMXNET3 Adapter on a Windows VM

LRO is enabled by default for VMXNET3 adapters on vSphere 6.0 hosts, but you must set RSC to be enabled globally for Windows 8 VMs. For more information about configuring this, see the documentation.


This blog shows that enabling LRO for Windows Server 2012 and Windows 8 VMs on a vSphere host using VMXNET3 considerably enhances the CPU efficiency and correspondingly improves throughput for TCP traffic.

Running Transactional Workloads Using Docker Containers on vSphere 6.0

In a series of blogs, we showed that not only can Docker containers seamlessly run inside vSphere 6.0 VMs, but both micro-benchmarks and popular workloads in such configurations perform as well as, and in some cases better than, in native Docker configurations.

See the following blog posts for our past findings:

In this blog, we study a transactional database workload and present the results on how Docker containers perform in a VM when we scale out the number of database instances. To do this experiment, we use DVD Store 2.1, which is an OLTP benchmark that supports and stresses many different back-end databases including Microsoft SQL Server, Oracle Database, MySQL, and PostgreSQL. This benchmark is open source and the latest version 2.1 is available here. It is a 3-tier application with a Web server, an application server, and a backend database server. The benchmark simulates a DVD store, where customers log in, browse, and order DVD products. The tool is designed to utilize a number of advanced database features including transactions, stored procedures, triggers, and referential integrity. The main transactions are (1) add new customers, (2) log in customers, (3) browse DVDs, (4) enter purchase orders, and (5) re-order stock. The client driver is written in C# and is usually run from Windows; however, the client can be run on Linux using the Mono framework. The primary performance metric of the benchmark is orders per minute (OPM).

In our experiments, we used a PostgreSQL database with an Apache Web server, and the application logic was implemented in PHP. In all tests we ran 16 instances of DVD Store, where each instance comprises all 3-tiers. We found that, due to better scheduling, running Docker in a VM in a scale-out scenario can provide better throughput than running a Docker container in a native system.

Next, we present the configurations, benchmarks, detailed setup, and the performance results.

Deployment Scenarios

We compare four different scenarios, as illustrated below:

–   Native: Linux OS running directly on hardware (Ubuntu 14.04.1)

–   vSphere VM: vSphere 6.0 with VMFS5, in 8 VMs, each with the same guest OS as native

–   Native-Docker: Docker 1.5 running on a native OS (Ubuntu 14.04.1)

–   VM-Docker: Docker 1.5 running in each of 8 VMs on a vSphere  host

In each configuration, all of the power management features were disabled in the BIOS.

Hardware/Software/Workload Configuration

Figure 1 shows the hardware setup diagram for the server host below. We used Ubuntu 14.04.1 with Docker 1.5 for all our experiments.  While running Docker configuration, we use bridged networking and host volumes for storing the database.


Figure 1. Hardware/software configuration

Performance Results

We ran 16 instances of DVD Store, where each instance was running an Apache web server, PHP application logic, and a PostgreSQL database. In the Docker cases, we ran one instance of DVD Store per Docker container. In the non-Docker cases, we used the Virtual Hosts functionality of Apache to run many instances of a Web server listening on different ports. We also used the PostgreSQL command line to create different instances of the database server listening on different ports. In the VM-based experiments, we partitioned the host hardware between 8 VMs, where each VM ran 2 DVD Store instances. The 8 VMs exactly committed the CPUs, and under-committed the memory.

The four configurations for our experiments are listed below.


  • Native-16S: 16 instances of DVD Store running natively (16 separate instances of Apache 2 using virtual hosts and 16 separate instances of PostgreSQL database)
  • Native-Docker-16S: 16 Docker containers running on a native machine with each running one instance of DVD Store.
  • VM-8VMs-16S: Eight 4-vCPU VMs each running 2 DVD Store instances
  • VM-Docker-8VMs-16S: Eight 4-vCPU VMs each running 2 Docker containers,  where each Docker container is running one instance of DVD Store

We ran the DVD Store benchmark for the 4 configurations using 16 client drivers, where each driver process was running 4 threads. The results for these 4 configurations are shown in the figure below.


Figure 2. DVD Store performance for different configurations

In the chart above, the y-axis shows the aggregate DVD Store performance metric orders per minute (OPM) for all 16 instances. We have normalized the order per minute results with respect to the native configuration where we saw about 126k orders per minute. From the chart, we see that, the VM configurations achieve higher throughput compared to the corresponding native configurations. As in the case of prior blogs, this is due to better NUMA-aware scheduling in vSphere.  We can also see that running Docker containers either natively or in VMs adds little overhead (2-4%).

To find out why the native configurations were not doing better, we pinned half of the Docker containers to one NUMA node and half to the other. The DVD Store aggregate OPM improved as a result and as expected, we were seeing slightly better than the VM configuration.  However, manually pinning processes to cores or sockets is usually not a recommended practice because it is error-prone and can, in general, lead to unexpected or suboptimal results.


In this blog, we showed that running a PostgreSQL transactional database in a Docker container in a vSphere VM adds very little performance cost compared to running directly in the VM. We also find that running Docker containers in a set of 8 VMs achieves slightly better throughput than running the same Docker containers natively with an out-of-the-box configuration. This is a further proof that VMs and Docker containers are truly “better together.”



Virtualized Storage Performance: RAID Groups versus Storage pools

RAID, a redundant array of independent disks, has traditionally been the foundation of enterprise storage. Grouping multiple disks into one logical unit can vastly increase the availability and performance of storage by protecting against disk failure, allowing greater I/O parallelism, and pooling capacity. Storage pools similarly increase the capacity and performance of storage, but are easier to configure and manage than RAID groups.

RAID groups have traditionally been regarded as offering better and more predictable performance than storage pools. Although both technologies were developed for magnetic hard disk drives (HDDs), solid-state drives (SSDs), which use flash memory, have become prevalent. Virtualized environments are also common and tend to create highly randomized I/O given the fact that multiple workloads are run simultaneously.

We set out to see how the performance of RAID group and storage pool provisioning methods compare in today’s virtualized environments.

First, let’s take a closer look at each storage provisioning type.

RAID Groups

A RAID group unifies a number of disks into one logical unit and distributes data across multiple drives. RAID groups can be configured with a particular protection level depending on the performance, capacity, and redundancy needs of the environment. LUNs are then allocated from the RAID group. RAID groups typically contain only identical drives, and the maximum number of disks in a RAID group varies by system model but is generally below fifty. Because drives typically have well defined performance characteristics, the overall RAID group performance can be calculated as the performance of all drives in the group minus the RAID overhead. To provide consistent performance, workloads with different I/O profiles (e.g., sequential vs. random I/O) or different performance needs should be physically isolated in different RAID groups so they do not share disks.

Storage Pools

Storage pools, or simply ‘pools’, are very similar to RAID groups in some ways. Implementation varies by vendor, but generally pools are made up of one or more private RAID groups, which are not visible to the user, or they are composed of user-configured RAID groups which are added manually to the pool. LUNs are then allocated from the pool. Storage pools can contain up to hundreds of drives, often all the drives in an array. As business needs grow, storage pools can be easily scaled up by adding drives or RAID groups and expanding LUN capacity. Storage pools can contain multiple types and sizes of drives and can spread workloads over more drives for a greater degree of parallelism.

Storage pools are usually required for array features like automated storage tiering, where faster SSDs can serve as a data cache among a larger group of HDDs, as well as other array-level data services like compression, deduplication, and thin provisioning. Because of their larger maximum size, storage pools, unlike RAID groups, can take advantage of vSphere 6 maximum LUN sizes of 64TB.

We used two benchmarks to compare the performance of RAID groups and storage pools: VMmark, which is a virtualization platform benchmark, and I/O Analyzer with Iometer, which is a storage microbenchmark.  VMmark is a multi-host virtualization benchmark that uses diverse application workloads as well as common platform level workloads to model the demands of the datacenter. VMs running a complete set of the application workloads are grouped into units of load called tiles. For more details, see the VMmark 2.5 overview. Iometer places high levels of load on the disk, but does not stress any other system resources. Together, these benchmarks give us both a ‘real-world’ and a more focused perspective on storage performance.

VMmark Testing

Array Configuration

Testing was conducted on an EMC VNX5800 block storage SAN with Fibre Channel. This was one of the many storage solutions which offered both RAID group and storage pool technologies. Disks were 200GB single-level cell (SLC) SSDs. Storage configuration followed array best practices, including balancing LUNs across Storage Processors and ensuring that RAID groups and LUNs did not span the array bus. One way to optimize SSD performance is to leave up to 50% of the SSD capacity unutilized, also known as overprovisioning. To follow this best practice, 50% of the RAID group or storage pool was not allocated to any LUN. Since overprovisioning SSDs can be an expensive proposition, we also tested the same configuration with 100% of the storage pool or RAID group allocated.

RAID Group Configuration

Four RAID 5 groups were used, each composed of 15 SSDs. RAID 5 was selected for its suitability for general purpose workloads. RAID 5 provides tolerance against a single disk failure. For best performance and capacity, RAID 5 groups should be sized to multiples of five or nine drives, so this group maintains a multiple of the preferred five-drive count. One LUN was created in each of the four RAID groups. The LUN was sized to either 50% of the RAID group (Best Practices) or 100% (Fully Allocated). For testing, the capacity of each LUN was fully utilized by VMmark virtual machines and randomized data.

            RAID Group Configuration VMmark Storage Comparison        VMmark Storage Pool Configuration Storage Comparison

Storage Pool Configuration

A single RAID 5 Storage Pool containing all 60 SSDs was used. Four thick LUNs were allocated from the pool, meaning that all of the storage space was reserved on the volume. LUNs were equivalent in size and consumed a total of either 50% (Best Practices) or 100% (Fully Allocated) of the pool capacity.

Storage Layout

Most of the VMmark storage load was created by two types of virtual machines: database (DVD Store) and mail server (Microsoft Exchange). These virtual machines were isolated on two different LUNs. The remaining virtual machines were spread across the remaining two LUNs. That is, in the RAID group case, storage-heavy workloads were physically isolated in different RAID groups, but in the storage pool case, all workloads shared the same pool.

Systems Under Test: Two Dell PowerEdge R720 servers
Configuration Per Server:  
     Virtualization Platform: VMware vSphere 6.0. VMs used virtual hardware version 11 and current VMware Tools.
     CPUs: Two 12-core Intel® Xeon® E5-2697 v2 @ 2.7 GHz, Turbo Boost Enabled, up to 3.5 GHz, Hyper-Threading enabled.
     Memory: 256GB ECC DDR3 @ 1866MHz
     Host Bus Adapter: QLogic ISP2532 DualPort 8Gb Fibre Channel to PCI Express
     Network Controller: One Intel 82599EB dual-port 10 Gigabit PCIe Adapter, one Intel I350 Dual-Port Gigabit PCIe Adapter

Each configuration was tested at three different load points: 1 tile (the lowest load level), 7 tiles (an approximate mid-point), and 13 tiles, which was the maximum number of tiles that still met Quality of Service (QoS) requirements. All datapoints represent the mean of two tests of each configuration.

VMmark Results

RAID Group vs. Storage Pool Performance comparison using VMmark benchmark

Across all load levels tested, the VMmark performance score, which is a function of application throughput, was similar regardless of storage provisioning type. Neither the storage type used nor the capacity allocated affected throughput.

VMmark 2.5 performance scores are based on application and infrastructure workload throughput, while application latency reflects Quality of Service. For the Mail Server, Olio, and DVD Store 2 workloads, latency is defined as the application’s response time. We wanted to see how storage configuration affected application latency as opposed to the VMmark score. All latencies are normalized to the lowest 1-tile results.

Storage configuration did not affect VMmark application latencies.

Application Latency in VMmark Storage Comparison RAID Group vs Storage Pool

Lastly, we measured read and write I/O latencies: esxtop Average Guest MilliSec/Write and Average Guest MilliSec/Read. This is the round trip I/O latency as seen by the Guest operating system.

VMmark Storage Latency Storage Comparison RAID Group vs Storage Pool

No differences emerged in I/O latencies.

I/O Analyzer with Iometer Testing

In the second set of experiments, we wanted to see if we would find similar results while testing storage using a synthetic microbenchmark. I/O Analyzer is a tool which uses Iometer to drive load on a Linux-based virtual machine then collates the performance results. The benefit of using a microbenchmark like Iometer is that it places heavy load on just the storage subsystem, ensuring that no other subsystem is the bottleneck.


Testing used a VNX5800 array and RAID 5 level as in the prior configuration, but all storage configurations spanned 9 SSDs, also a preferred drive count. In contrast to the prior test, the storage pool or RAID group spanned an identical number of disks, so that the number of disks per LUN was the same in both configurations. Testing used nine disks per LUN to achieve greater load on each disk.

The LUN was sized to either 50% or 100% of the storage group. The LUN capacity was fully occupied with the I/O Analyzer worker VM and randomized data.  The I/O Analyzer Controller VM, which initiates the benchmark, was located on a separate array and host.

Storage Configuration Iometer with Storage Pool and RAID Group

Testing used one I/O Analyzer worker VM. One Iometer worker thread drove storage load. The size of the VM’s virtual disk determines the size of the active dataset, so a 100GB thick-provisioned virtual disk on VMFS-5 was chosen to maximize I/O to the disk and minimize caching. We tested at a medium load level using a plausible datacenter I/O profile, understanding, however, that any static I/O profile will be a broad generalization of real-life workloads.

Iometer Configuration

  • 1 vCPU, 2GB memory
  • 70% read, 30% write
  • 100% random I/O to model the “I/O blender effect” in a virtualized environment
  • 4KB block size
  • I/O aligned to sector boundaries
  • 64 outstanding I/O
  • 60 minute warm up period, 60 minute measurement period
Systems Under Test: One Dell PowerEdge R720 server
Configuration Per Server:  
     Virtualization Platform: VMware vSphere 6.0. Worker VM used the I/O Analyzer default virtual hardware version 7.
     CPUs: Two 12-core Intel® Xeon® E5-2697 v2 @ 2.7 GHz, Turbo Boost Enabled, up to 3.5 GHz, Hyper-Threading enabled.
     Memory: 256GB ECC DDR3 @ 1866MHz
     Host Bus Adapter: QLogic ISP2532 DualPort 8Gb Fibre Channel to PCI Express

Iometer results

Iometer Latency Results Storage Comparison RAID Group vs Storage PoolIometer Throughput Results Storage Comparison RAID Group vs Storage Pool

In Iometer testing, the storage pool showed slightly improved performance compared to the RAID group, and the amount of capacity allocated also did not affect performance.

In both our multi-workload and synthetic microbenchmark scenarios, we did not observe any performance penalty of choosing storage pools over RAID groups on an all-SSD array, even when disparate workloads shared the same storage pool. We also did not find any performance benefit at the application or I/O level from leaving unallocated capacity, or overprovisioning, SSD RAID groups or storage pools. Given the ease of management and feature-based benefits of storage pools, including automated storage tiering, compression, deduplication, and thin provisioning, storage pools are an excellent choice in today’s datacenters.

Scaling Web 2.0 Applications using Docker containers on vSphere 6.0

by Qasim Ali

In a previous VROOM post, we showed that running Redis inside Docker containers on vSphere adds little to no overhead and observed sizeable performance improvements when scaling out the application when compared to running containers directly on the native hardware. This post analyzes scaling Web 2.0 applications using Docker containers on vSphere and compares the performance of running Docker containers on native and vSphere. This study shows that Docker containers add negligible overhead when run on vSphere, and also that the performance using virtual machines is very close to native and, in certain cases, slightly better due to better vSphere scheduling and isolation.

Web 2.0 applications are an integral part of Enterprise and small business IT offerings. We use the CloudStone benchmark, which simulates a typical Web 2.0 technology use in the workplace for our study [1] [2]. It includes a Web 2.0 social-events application (Olio) and a client implemented using the Faban workload generator [3]. It is an open source benchmark that simulates activities related to social events. The benchmark consists of three main components: a Web server, a database backend, and a client to emulate real world accesses to the Web server. The overall architecture of CloudStone is depicted in Figure 1.

Figure 1: CloudStone architecture

The benchmark reports latency for various user actions. These metrics were compared against a fixed threshold. Studies indicate that users are less likely to visit a Web site if the response time is greater than 250 milliseconds [4]. This number can be used as an upper bound for latency for frequent operations (Home-Page, TagSearch, EventDetail, and Login). For the less frequent operations (AddEvent, AddPerson, and PersonDetail), a less restrictive threshold of 500 milliseconds can be used. Table 1 shows the exact mix/frequency of various operations.

Operation Number of Operations Mix
HomePage 141908 26.14%
Login 55473 10.22%
TagSearch 181126 33.37%
EventDetail 134144 24.71%
PersonDetail 14393 2.65%
AddPerson 4662 0.86%
AddEvent 11087 2.04%

Table 1: CloudStone operations frequency for 1500 users

Benchmark Components and Experimental Set up

The test system was installed with a CloudStone implementation of a MySQL database, NGINX Web server with PHP scripts, and a Tomcat application server provided by the Faban harness. The default configuration was used for the workload generator. All components of the application ran on a single host, and the client ran in a separate virtual machine on a separate host. Both hosts were connected using a direct link between a pair of 10Gbps NICs. One client-server pair provided a single, independent CloudStone instance. Scaling was achieved by running additional instances of CloudStone.

Deployment Scenarios

We used the following three deployment scenarios for this study:

  • Native-Docker: One or more CloudStone instances were run inside Docker containers (2 containers per CloudStone instance: one for the Web server and another for the database backend) running on the native OS.
  • VM: CloudStone instances were run inside one or more virtual machines running on vSphere 6.0; the guest OS is the same as the native scenario.
  • VM-Docker: CloudStone instances were run inside Docker containers that were running inside one or more virtual machines.

Hardware/Software/Workload Configuration

The following are the details about the hardware and software used in the various experiments discussed in the next section:

Server Host:

  • Dell PowerEdge R820
  • CPU: 4 x Intel® Xeon® CPU E5-4650 @ 2.30GHz (32 cores, 64 hyper-threads)
  • Memory: 512GB
  • Hardware configuration: Hyper-Threading (HT) ON, Turbo-boost ON, Power policy: Static High (that is, no power management)
  • Network: 10Gbps
  • Storage: 7 x 250GB 15K RPM 4Gb SAS  Disks

Client Host:

  • Dell PowerEdge R710
  • CPU: 2 x Intel® Xeon® CPU X5680 @ 3.33GHz (12 cores, 24 hyper-threads)
  • Memory: 144GB
  • Hardware configuration: HT ON, Turbo-boost ON, Power policy: Static High (that is, no power management)
  • Network: 10Gbps
  • Client VM: 2-vCPU 4GB vRAM

Host OS:

  • Ubuntu 14.04.1
  • Kernel 3.13

Docker Configuration:

  • Docker 1.2
  • Ubuntu 14.04.1 base image
  • Host volumes for database and images
  • Configured with host networking to avoid Docker NAT overhead
  • Device mapper as the storage backend driver


  • VMware vSphere 6.0 (pre-release build)

VM Configurations:

  • Single VM: An 8-vCPU 4GB VM ( Web server and database running in a single VM)
  • Two VMs: One 6-vCPU 2GB Web server VM and one 2-vCPU 2GB database VM (CloudStone instance running in two VMs)
  • Scale-out: Eight 8-vCPU 4GB VMs

Workload Configurations:

  • The NGINX Web server was configured with 4 worker processes and 4096 connections per worker.
  • PHP was configured with a maximum number of 16 child processes.
  • The Web server and the database were preconfigured with 1500 users per CloudStone instance.
  • A runtime of 30 minutes with a 5 minute ramp-up and ramp-down periods (less than 1% run-to-run  variation) was used.


First, we ran a single instance of CloudStone in the various configurations mentioned above. This was meant to determine the raw overhead of Docker containers running on vSphere vs. the native configuration, eliminating scheduling differences. Second, we picked the configuration that performed best in a single instance and scaled it out to run multiple instances.

Figure 2 shows the mean latency of the most frequent operations and Figure 3 shows the mean latency of less frequent operations.

Figure 2: Results of single instance CloudStone experiments for frequently used operations


Figure 3: Results of single instance CloudStone experiments for less frequently used

We configured the benchmark to use a single VM and deployed the Web server and database applications in it (configuration labelled VM-1VM in Figure 2 and 3). We then ran the same workload in Docker containers in a single VM (VMDocker-1VM). The latencies are slightly higher than native, which is expected due to some virtualization overhead. However, running Docker containers on a VM showed no additional overhead. In fact, it seems to be slightly better. We believe this might be due to the device mapper using twice as much page cache as the VM (device mapper uses a loopback device to mount the file system and, hence, data ends up being cached twice in the buffer cache). We also tried AuFS as a storage backend for our container images, but that seemed to add some CPU and latency overhead, and, for this reason, we switched to device mapper. We then configured the VM to use the vSphere Latency Sensitivity feature [5] (VM-1VM-lat and VMDocker-1VM-lat labels). As expected, this configuration reduced the latencies even further because each vCPU got exclusive access to a core and this reduced scheduling overhead.  However, this feature cannot be used when the VM (or VMs) has more vCPUs than the number of cores available on the system (that is, the physical CPUs are over-committed) because each vCPU needs exclusive access to a core.

Next, we configured the workload to use two VMs, one for the Web server and the other for the database application. This configuration ended up giving slightly higher latencies because the network packets have to traverse the virtualization layer from one VM to the other, while in the prior experiments they were confined within the same VM.

Finally, we scaled out the CloudStone workload with 12,000 users by using eight 8-vCPU VMs with 1500 users per instance. The VM configurations were the same as the VM-1VM and VMDocker-1VM cases above. The average system CPU core utilization was around 70-75%, which is the typical average CPU utilization for latency sensitive workloads because it allows for headroom to absorb traffic bursts. Figure 4 reports mean latencies of all operations (latencies were averaged across all eight instances of CloudStone for each operation), while Figure 5 reports the 90th percentile latencies (the benchmark reports these latencies in 20 millisecond granularity as evident from Figure 5.)


Figure 4: Scale-out experiments using eight instances of CloudStone (mean latency)


Figure 5: Scale-out experiments using eight instances of CloudStone (90th percentile latency)

The latencies shown in Figure 4 and 5 are well below the 250 millisecond threshold. We observed that the latencies on vSphere are very close to native or, in certain cases, slightly better than native (for example, Login, AddPerson and AddEvent operations). The latencies were better than native due to better vSphere scheduling and isolation, resulting in better cache/memory locality. We verified this by pinning container instances on specific sockets and making the native scheduler behavior similar to vSphere. After doing that, we observed that latencies in the native case got better and they were similar or slightly better than vSphere.

Note: Introducing artificial affinity between processes and cores is not a recommended practice because it is error-prone and can, in general, lead to unexpected or suboptimal results.


VMs and Docker containers are truly “better together.” The CloudStone scale-out system, using out-of-the-box VM and VM-Docker configurations, clearly achieves very close to, or slightly better than, native performance.


[1] W. Sobel, S. Subramanyam, A. Sucharitakul, J. Nguyen, H. Wong, A. Klepchukov, S. Patil, O. Fox and D. Patterson, “CloudStone: Multi-Platform, Multi-Languarge Benchmark and Measurement Tools for Web 2.8,” 2008.
[2] N. Grozev, “Automated CloudStone Setup in Ubuntu VMs / Advanced Automated CloudStone Setup in Ubuntu VMs [Part 2],” 2 June 2014. https://nikolaygrozev.wordpress.com/tag/cloudstone/.
[3] java.net, “Faban Harness and Benchmark Framework,” 11 May 2014. http://java.net/projects/faban/.
[4] S. Lohr, “For Impatient Web Users, an Eye Blink Is Just Too Long to Wait,” 29 February 2012. http://www.nytimes.com/2012/03/01/technology/impatient-web-usersflee-slow-loading-sites.html.
[5] J. Heo, “Deploying Extremely Latency-Sensitive Applications in VMware vSphere 5.5,” 18 September 2013.   http://blogs.vmware.com/performance/2013/09/deploying-extremely-latency-sensitive-applications-in-vmware-vsphere-5-5.html.




VMware Horizon 6 with View: Performance Testing and Best Practices

In the blog here, we just published the updated white paper VMware Horizon 6 with View Performance and Best Practices that describes the performance gains achieved with the latest Horizon 6 enhancements. The paper details the architecture systems used for testing the features and recommends best practices for configuring your system.

The white paper talks about the following performance results:

  • RDSH sizing
  • Display protocol performance
  • PCoIP default settings changes
  • VDI characterization on Virtual SAN

Finally, some of the best practices are presented:

  • RDSH virtual machine sizing
  • RDSH session sizing
  • RDSH server virtual machine optimization
  • Guest best practices for bandwidth and storage
  • PCoIP settings
  • 3D graphics settings
  • Virtual SAN configurations

Please check out the VMware Horizon 6 with View Performance and Best Practices  paper for detailed descriptions of the tests, the results of those tests and the best practices.


VMware View Planner 3.5 and Use Cases

by   Banit Agrawal     Nachiket Karmarkar

VMware View Planner 3.5 was recently released which introduces a slew of new features, enhancements in user experience, and scalability. In this blog, we present some of these new features and use cases. More details can be found in the whitepaper here.

In addition to retaining all the features available in VMware View Planner 3.0, View Planner 3.5 addresses the following new use cases:

  • Support for VMware Horizon 6  (support of RDSH session and application publishing)
  • Support for Windows 8.1 as desktops
  • Improved user experience
  • Audio-Video sync (AVBench)
  • Drag and Scroll workload (UEBench)
  • Support for Windows 7 as clients

In View Planner 3.5, we augment the capability of View Planner to quantify user experience for user sessions and application remoting provided through remote desktop session hosts (RDSH) as a sever farm. Starting this release, we will support Windows 8.1 as one of the supported guest OSes for desktops and Windows 7 as the supported guest OS for clients.

New Interactive Workloads

We also introduced two advanced workloads: (1) Audio-Video sync (AVBench) and (2) Drag and Scroll workload (UEBench). AVBench determines audio fidelity in a distributed environment where audio and video streams are not tethered. The “Drag and Scroll” workload determines spatial and temporal variance by emulating user events like mouse click, scroll, and drag.


Fig 1. Mouse-click and drag  (UEBench)

As seen in Figure 1, a mouse event is sent to the desktop and the red and black image is dragged across and down the screen.


Fig. 2. Mouse-click and scroll (UEBench)

Similarly, Figure 2 depicts a mouse event sent to the scroll bar of an image that is scrolled up and down.

Better Run Status Reporting

As part of improving the user experience, the UI can track the current stage the View Planner run is in and notifies the user through a color-coded box. The text inside the box is a clickable link that provides a pop-up giving deeper insight about that particular stage.


Fig. 3. View Planner run status shows the intermediate status of the run

Pre-Check Run Profile for Errors

A “check” button provides users a way to verify the correctness of their run-profile parameters.


Fig. 4. ‘Check’ button in Run & Reports tab in View Planner UI

 In the past, users needed to optimize the parent VMs used for deploying clients and desktop pools. View Planner 3.5 has automated these optimizations as part of installing the View Planner agent service. The agent installer also comes with a UI that tracks the current stage the installer is in and highlights the results of various installer stages.

Sample Use Cases

Single Host VDI Scaling

Through this release, we have re-affirmed the use case of View Planner as an ideal tool for platform characterization for VDI scenarios.  On a Cisco UCS C240 server, we started with a small number of desktops running the “standard benchmark profile” and increased them until the Group A and Group B numbers exceeded the threshold. The results below demonstrate the scalability of a single UCS C240 server as a platform for VDI deployments.


Fig. 5. Single server characterization with hosted desktops for CISCO UCS C240

Single Host RDSH Scaling

We followed the best practices prescribed in the VMware Horizon 6 RDSH Performance & Best Practices whitepaper  and set up a number of remote desktop session (RDS) servers that would fully consolidate a single UCS C240 vSphere server. We started with a small number of user sessions per core and then increased them until the Group A and Group B numbers exceeded the threshold level. The results below demonstrate how ViewPlanner can accurately gauge the scalability of a platform (CISCO UCS in this case) when deployed in an RDS scenario


Fig. 6. Single server characterization with RDS sessions for CISCO UCS C240

Storage Characterization

View Planner can also be used to characterize storage array performance. The scalability of View Planner 3.5 to drive a workload on thousands of virtual desktops and process the results thereafter makes it an ideal candidate to validate storage array performance at scale. The results below demonstrate scalability of VDI desktops obtained on Pure Storage FA-420 all-flash array. View Planner 3.5 could easily scale to 3000 desktops, as highlighted in the results below.


Fig. 7. 3000 Desktops QoS results on Pure Storage FA-420 storage array

Custom Applications Scaling

In addition to characterizing platform and storage arrays, the custom app framework can achieve targeted VDI characterization that is application specific. The following results show Visio as an example of a custom app scale study on an RDS deployment with a 4-vCPU, 10GB vRAM Windows 2008 Server.


Fig. 8 Visio operation response times with View Planner 3.5 when scaling up application sessions

Other Use Cases

With a plethora of features, supported guest OSes, and configurations, it is no wonder that View Planner is capable to of characterizing multiple VMware solutions and offerings that work in tandem with VMware Horizon. View Planner 3.5 can also be used to evaluate the following features, which are described in more detail in the whitepaper:

  • VMware Virtual SAN
  • VMware Horizon Mirage
  • VMware App Volumes

For more details about new features, use cases, test environment, and results, please refer to the View Planner 3.5 white paper here.