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Monthly Archives: October 2013

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.