vSphere 5.1 introduced an inventory tagging feature that has been available in all later versions of vSphere, including vSphere 6.7. Tags let datacenter administrators organize different vSphere objects like datastores, virtual machines, hosts, and so on. This makes it easier to sort and search for objects that share a tag, among other things. For example, you might use tags to track a group of VMs that all have the same operating system.
Writing code to use tags can be challenging in large-scale environments: a straightforward use of VMware PowerCLI cmdlets may result in poor performance, and while direct Tagging Service APIs are faster, the documentation can be difficult to understand. In this blog, we show some practical examples of using PowerCLI and Tagging Service APIs to perform tag-related operations. We include some simple measurements to show the performance improvements when using the Tagging Service vs. cmdlets. The sample performance numbers are for illustrative purposes only. We describe the test setup in the Appendix.
1. Connecting to PowerCLI and the Tagging Service
In this document, when we write “PowerCLI cmdlets,” we mean calls like Get-Tag, or Get-TagCategory. To access this API, simply open a PowerShell terminal and log in:
Connect-VIServer <vCenter server IP or FQDN> -User <username> -Pass <password>
Along with the recent release of VMware vSphere 6.7 U2, we published a new whitepaper that shows the performance of a new scheduler option that was included in the 6.7 U2 update. We referred to this new scheduler option internally as the “sibling” scheduler, but the official name is the side-channel aware scheduler version 2, or SCAv2. The whitepaper includes full details about SCAv1 and SCAv2, the L1TF security vulnerability that made them necessary, and the performance implications with several different workload types. This blog is a brief overview of the key points, but we recommend that you check out the full document.
In August of 2018, a security vulnerability known as L1TF, affecting systems using Intel processors, was revealed, and patches and remediations were also made available. Intel provided micro-code updates for its processors, operating system patches were made available, and VMware provided an update for vSphere. The full details of the vCenter and ESXi patches are in a VMware security advisory that links to individual KB articles.
VMmark is a free tool used by hardware vendors and others to measure the performance, scalability, and power consumption of virtualization platforms. If you’re unfamiliar with VMmark 3.x, each tile is a grouping of 19 virtual machines (VMs) simultaneously running diverse workloads commonly found in today’s data centers, including a scalable Web simulation, an E-commerce simulation (with backend database VMs), and standby/idle VMs.
As Joshua mentioned in a recent blog post, we released VMmark 3.1 in February, adding support for persistent memory, improving workload scalability, and better reflecting secure customer environments by increasing side-channel vulnerability mitigation requirements.
I’m happy to announce that today we published the first VMmark 3.1 results. These results were obtained on systems meeting our industry-leading side-channel-aware mitigation requirements, thus continuing the benchmark’s ability to provide an indication of real-world performance.
The IoT Analytics Benchmark released last year dealt with an important Internet of Things use case—monitoring factory sensor data for impending failure conditions. This year, we are tackling an equally important use case—image classification. Whether used in facial recognition, license plate readers, inspection systems, or autonomous vehicles, neural network–based deep learning is making image detection and classification a viable technology.
As in the classic machine learning used in the original IoT Analytics Benchmark code (which used the Spark Machine Learning Library), the new deep learning code first trains a model using pre-labeled images and then deploys that model to infer the classification of new images. For IoT this inference step is the most important. Thus, the new programs, designated as IoT Analytics Benchmark DL, use previously trained models (included in the kit) to demonstrate inferencing that can be performed at the edge (on small gateway systems) or in scaled-out Spark clusters.
VMware Cloud on AWS is a hybrid cloud service that runs the VMware software-defined data center (SDDC) stack in the Amazon Web Services (AWS) public cloud. The service automatically provisions and deploys a vSphere environment on a bare-metal AWS infrastructure, and lets you run your applications in a hybrid IT environment across your on-premises data centers and AWS global infrastructure. A key benefit of VMware Cloud on AWS is the ability to vMotion workloads back and forth from your on-premises data center to the AWS public cloud as capacity and data privacy require.
In this blog post, we share the results of our vMotion performance tests across our hybrid cloud environment that consisted of a vSphere on-premises data center located in Wenatchee, Washington and an SDDC hosted in an AWS cloud, in various scenarios including hybrid migration of a database server. We also describe the best practices to follow when migrating virtual machines by vMotion across hybrid cloud.
The vSAN Performance Diagnostics feature, which helps customers to optimize their benchmarks or their vSAN configurations to achieve the best possible performance, was first introduced in vSphere 6.5 U1. vSAN Performance Diagnostics is a “cloud connected” feature and requires participation in the VMware Customer Experience Improvement Program (CEIP). Performance metrics and data are collected from the vSAN cluster and are sent to the VMware Cloud. The data is analyzed and the results are sent back for display in the vCenter Client. These results are shown as performance issues, where each issue includes a problem with its description and a link to a KB article.
In this blog, we describe how vSAN Performance Diagnostics can be used with HCIBench and show the new feature in vSphere 6.7 U1 that provides HCIBench specific issues and recommendations.
What is HCIBench?
HCIBench (Hyper-converged Infrastructure Benchmark) is a standard benchmark that vSAN customers can use to evaluate the performance of their vSAN systems. HCIBench is an automation wrapper around the popular and proven VDbench open source benchmark tool that makes it easier to automate testing across an HCI cluster. HCIBench, available as a fling, simplifies and accelerates customer performance testing in a consistent and controlled way.
Virtual machine (VM) provisioning operations such as create, clone, and relocate involve the placement of storage resources. Storage DRS (sometimes seen as “SDRS”) is the resource management component in vSphere responsible for optimal storage placement and load balancing recommendations in the datastore cluster.
A key contributor to VM provisioning times in Storage DRS-enabled environments is the time it takes (latency) to receive placement recommendations for the VM disks (VMDKs). This latency particularly comes into play when multiple VM provisioning requests are issued concurrently.
Several changes were made in vSphere 6.7 to improve the time to generate placement recommendations for provisioning operations. Specifically, the level of parallelism was improved for the case where there are no storage reservations for VMDKs. This resulted in significant improvements in recommendation times when there are concurrent provisioning requests.
vRealize automation suite users who use blueprints to deploy large numbers of VMs quickly will notice the improvement in provisioning times for the case when no reservations are used.
Several performance optimizations were further made inside key steps of processing the Storage DRS recommendations. This improved the time to generate recommendations, even for standalone provisioning requests with or without reservations.
PbmCheckCompliance is automatically invoked soon after provisioning operations such as creating, cloning, and relocating a VM. It is also automatically triggered in the background once every 8 hours to help keep the compliance records up-to-date.
Data scientists may use GPUs on vSphere that are dedicated to use by one virtual machine only for their modeling work, if they need to. Certain heavier machine learning workloads may well require that dedicated approach. However, there are also many ML workloads and user types that do not use a dedicated GPU continuously to its maximum capacity. This presents an opportunity for shared use of a physical GPU by more than one virtual machine/user. This article explores the performance of a shared-GPU setup like this, supported by the NVIDIA GRID product on vSphere, and presents performance test results that show that sharing is a feasible approach. The other technical reasons for sharing a GPU among multiple VMs are also described here. The article also gives best practices for determining how the sharing of a GPU may be done.
VMware vSphere supports NVIDIA GRID technology for multiple types of workloads. This technology virtualizes GPUs via a mediated passthrough mechanism. Initially, NVIDIA GRID supported GPU virtualization for graphics workloads only. But, since the introduction of Pascal GPU, NVIDIA GRID has supported GPU virtualization for both graphics and CUDA/machine learning workloads. With this support, multiple VMs running GPU-accelerated workloads like machine learning/deep learning (ML/DL) based on TensorFlow, Keras, Caffe, Theano, Torch, and others can share a single GPU by using a vGPU provided by GRID. This brings benefits in multiple use cases that we discuss on this post.