VM SERVICE - HELP DEVELOPERS BY ALIGNING THEIR KUBERNETES NODES TO THE PHYSICAL INFRASTRUCTURE
The vSphere 7.0 U2a update released on April 27th introduces the new VM service and VM operator. Hidden away in what seems to be a trivial update is a collection of all-new functionalities. Myles Gray has written an extensive article about the new features. I want to highlight the administrative controls of VM classes of the VM service. VM Classes What are VM classes, and how are they used? With Tanzu Kubernetes Grid Service running in the Supervisor cluster, developers can deploy Kubernetes clusters without the help of the Infra Ops team. Using their native tooling, they specify the size of the cluster control plane and worker nodes by using a specific VM class. The VM class configuration acts a template used to define CPU, memory resources and possibly reservations for these resources. These templates allow the InfraOps team to set guardrails for the consumption of cluster resources by these TKG clusters.
VSPHERE WITH TANZU VCENTER SERVER NETWORK CONFIGURATION OVERVIEW
I noticed quite a few network-related queries about the install of vSphere with Tanzu together with vCenter Server Networks (Distributed Switch and HA-Proxy). The whole install process can be a little overwhelming when it’s your first time dealing with Kubernetes and load-balancing services. Before installing Workload Management (the user interface designation for running Kubernetes inside vSphere), you have to setup HA-Proxy, a virtual appliance for provisioning load-balancers. Cormac did a tremendous job describing the configuration steps of all components. Still, when installing vSphere with Tanzu, I found myself mapping out the network topology to make sense of it all since there seems to be a mismatch of terminology between HA-proxy and Workload Management configuration workflow.
WHAT'S YOUR FAVORITE TECH NOVEL?
Today I was discussing with Duncan some great books to read. Disconnecting fully from work is difficult for me, so typically, the books I read are tech-related. I have read some brilliant books that I want to share with you, but mostly I want to hear your recommendations for other excellent tech novels. Cyberwarfare Cyberwarfare intrigues me, so any book covering Operation Olympic Games -or- Stuxnet interests me. One of the best books on this topic “Countdown to Zero day” by Kim Zetter. The book is filled with footnotes and references to corresponding research.
NEW WHITEPAPER AVAILABLE ON VSPHERE 7 DRS LOAD BALANCING
vSphere 7 contains the new DRS algorithm that differs tremendously from the old one. The performance team has put the new algorithm through the test and have published a whitepaper presenting their findings. Read the white paper: Load Balancing Performance of DRS in vSphere 7.0.
VSPHERE 7 DRS SCALABLE SHARES DEEP DIVE
You are one tickbox away from completely overhauling the way you look at resource pools. Yes you can still use them as folders (sigh), but with the newly introduced Scalable Shares option in vSphere 7 you can turn resource pools into more or less Quality of Service classes. Sounds interesting right? Let’s first take a look at the traditional working of a resource pool, the challenges they introduced, and how this new delivery of resource distribution works. To understand that we have to take a look at the basics of how DRS distributes unreserved resources first.
VSPHERE 7 VMOTION WITH ATTACHED REMOTE DEVICE
A lot of cool new features fly under the radar during a major release announcement. Even the new DRS algorithm didn’t get much air time. One thing that I discovered this week, is that vSphere 7 allows for vMotion with an attached remote device attached. When using the VM Remote Console, you can attach ISOs stored on your local machine to the VM. An incredibly useful tool that allows you to quickly configure a VM.
DRS MIGRATION THRESHOLD IN VSPHERE 7
DRS in vSphere 7 is equipped with a new algorithm. The old algorithm measured the load of each host in the cluster and tried to keep the difference in workload within a specific range. And that range, the target host load standard deviation, was tuned via the migration threshold. The new algorithm is designed to find an efficient host for the VM that can provide the resources the workload demand while considering the potential behavior of other workloads. Throughout a series of articles, I will explain the actions of the algorithm in more detail.
VSPHERE SUPERVISOR NAMESPACE
vSphere 7 with Kubernetes enables the vSphere cluster to run and manage containers as native constructs (vSphere Pods). The previous two articles in this series cover the initial placement of a vSphere pod and compute resource management of individual vSphere pods. This article covers the compute resource management aspect of the vSphere Supervisor namespace construct. Cormac Hogan will dive into the storage aspects of the Supervisor namespace in his (excellent) blog series.
SCHEDULING VSPHERE PODS
The previous article “Initial Placement of a vSphere Pod,” covered the internal construction of a vSphere Pod to show that it is a combination of a tailor-made VM construct and a group of containers. Both the Kubernetes and vSphere platforms contain a rich set of resource management controls, policies, and features that guide, control, or restrict scheduling of workloads. Both control planes use similar types of expressions, creating a (false) sense of unification in the context of managing a vSphere pod. This series of articles examines the overlap of the different control plane dialects, and how the new vSphere 7 platform allows the developer to use Kubernetes native expressions, while the vSphere Admin continues to use the familiar vSphere native resource management functionalities.
DEEP LEARNING TECHNOLOGY STACK OVERVIEW FOR THE VADMIN - PART 1
Introduction We are amid the AI “gold rush.” More organizations are looking to incorporate any form of machine learning (ML) or deep learning in their services to enhance customer experience, drive efficiencies in their processes or improve quality of life (healthcare, transportation, smart cities). Train Where Data is Generated One of the key elements that drive on-premise ML focused infrastructure growth is the reality of data gravity. Mentioned in the article “Multi-GPU and Distributed Deep Learning,” deep learning (DL) gets better with data. Consequently, data sets used for ML and DL training purposes are growing at a tremendous rate. These vast data sets need to be processed. Data transit, hosting, and the necessary compute cycles impact the overall OPEX budget. Additionally, data protection regulations such as data residency, data sovereignty, and data locality impact where data can be stored outside the place it is created. As a result, a lot of forward-leaning organizations are repatriating their AI platforms to run ML and DL workloads close to the systems that generate the data.