Author: David (Page 15 of 24)

Introducing VMware Kubernetes Engine

June 27, 2018 / David / 0 Comments

On the 26th of June 2018, VMware publically announced VKE – VMware Kubernetes Engine in Beta (with GA planned for later on this year). For me, the development of this solution flew under the radar, and its subsequent release came as quite a surprise – albeit quite a good one. So, where exactly does this solution fit with other Kubernetes based solutions that currently exist?

VKE Overview

VKE sits within VMware’s portfolio of cloud-native solutions as is pitched as a fully managed, Kubernetes-as-a-service offering. Therefore we have multiple ways we can consume Kubernetes resources from the VMware ecosystem, depicted in the diagram below.

Which prompts some customers to ask – Why should I pick VKS over PKS or vice-versa? From a high level, some of the differences are listed below:

	PKS	VKS
Management Responsibility	Customer/Enterprise	Fully Managed
Consumption Model	Install, Configure, Manage, Consume	Consume
Residence	Public and Private Cloud	Public Cloud only

What we can ascertain here is that VKE is designed to abstract away all the infrastructure components that are required for an operational Kubernetes deployment. As a reminder, PKS is composed of:

PKS Control Plane
Kubernetes core
BOSH
Harbor
GCP Service Broker
NSX-T

Which is quite a lot to manage and maintain. VKS however, takes away the requirement for us, the customers to manage such entities, and simply provides a Kubernetes endpoint for us to consume. Networking, storage and other aspects are abstracted. Of course, there are use cases for both VKE and PKS. VKE is not looking to be a replacement for PKS.

How does it work?

Under the hood, VKS is deployed on top of AWS (who recently announced EKS, Amazon’s own managed Kubernetes-as-a-service platform) but in fitting with VMware’s ethos of “Any app, any cloud”, this is likely to extend to other cloud platforms – notably Azure. In addition to simply leveraging the AWS backend, VKS adds a few new features:

VMware Smart Cluster – Essentially, this is a layer of resource management, designed to automate the allocation of compute resources for maximum efficiency and cost saving, as well as automatic remediation of nodes.
Full end-to-end encryption – Designed so that all data, be it in transit or at rest is encrypted by default.
Role based access control – Map enterprise users to clusters.
Integration with Amazon Services – EC2, Lambda, S3, ES, Machine learning, the list is extensive.
Integration with VMware cloud services – Log insight, Wavefront, Cost insight, etc.

Why shouldn’t I just use EKS if I wanted an AWS-backed Kubernetes instance?

To be honest, this is a good question. If you aren’t using any VMware services currently, then it makes sense to go with EKS. However, existing VMware customers can potentially gain a consistent operational experience with both on-premises and cloud-based resources using familiar tools. Plus, when VKS opens up to other cloud providers, this will add tremendous agility to the placement of Kubernetes workloads, facilitating a true multi-cloud experience.

This service is obviously very new, and no doubt will change a bit up to GA, but it’s definitely worth keeping an eye on, considering the growing adoption of Kubernetes in general.

Hybrid Cloud monitoring with VMware vRealize Operations

June 12, 2018 / David / 0 Comments

Applications and the underlying infrastructure, be it public, private or hybrid cloud are becoming increasingly sophisticated. Because of this, the way in which we monitor and observe these environments requires more sophisticated tools. In this blog post, we look at vRealize Operations and how it can be a facilitator of true hybrid cloud monitoring.

What is vRealize Operations?

vRealize Operations forms part of the overall vRealize suite from VMware – a collection of products targeted to accommodate cloud management and automation. In particular, vRealize Operations, as the name implies, primarily caters to operations management with full visibility across physical, virtual and cloud-based environments. The anatomy of vRealize Operations is depicted below

Integrated Cloud Operations Console – A single, unified frontend to access, modify and view all related vRealize Operations components.

Integrated Management Disciplines – vRealize Operations has built-in intelligence to assimilate, dissect and report back on a number of key operational metrics pertaining to performance, capacity, planning and more. Essentially, vRealize Operations “learns” about your environment and is able to make recommendations, predictions and much more based on your specific workloads.

Platform Services – vRealize Operations is able to perform a number of platform management disciplines based on your specific environment. As an example, vRealize Operations can automate the addition of virtual machine memory based on monitored load, therefore proactively addressing potential issues before they surface.

Extensibility – Available from the VMware Marketplace, Management Packs extend the functionality of vRealize Operations. Examples include:

Microsoft Azure Management Pack from Blue Medora
AWS Management Pack from VMware
Docker Management Pack from Blue Medora
Dell | EMC Management Pack from Blue Medora
vRealize Operations Compliance Pack for PCI from VMware

The examples above demonstrate vRealize Operation’s capability to monitor AWS and Azure environments in addition to on-premises workloads, making vRealize Operations a true platform for Hybrid Cloud monitoring and operations management

Practical Example – Cluster Monitoring / Troubleshooting

In this example, we leverage one of the vRealize Operation’s built-in dashboards to check the performance of a specific cluster. A dashboard in vRealize operations terminology is a collection of objects and their state, represented in a visual fashion.

One of the ways vRealize understands the underlying environment is to establish and map dependencies in a logical manner. In this example, we have a top-level datacentre object (ISH), which child objects are decedents of (Cluster and hosts) this dashboard identifies key aspects of this cluster in a single page:

Cluster activity / utilisation
Health state of associated objects
CPU contention information
Memory contention information
Disk latency information

Without vRealize Operations it would be common for an administrator to try and collate these metrics manually, looking at individual performance charts, DRS scheduling information, and vCenter health alarms. However, with vRealize operations, this data is collected and centralised for easy and effortless exposure.

Practical Example – Workload Planning

In this example, we have an upcoming project that we want to forecast into our environment, particularly around disk space demand. We facilitate this by creating a “Project” in vRealize Operations, but before that, let’s look at the project UI in a bit more detail:

We can access this section by navigating to Environment > vSphere Object. At which point we can select the resource we’re interested in forecasting into. The chart in the middle projects the disk space demand for this specific vSphere object (a cluster, in this example). Note how we have an incline in disk space demand, which is typical of a production environment, however, we are within capacity for the time period specified (90 days).

To add a project, we click the green “plus” icon below the chart:

Next, we fill in details pertaining to the demand. In this case, I’m adding demand in the form of 5 virtual machines and I’m populating the specification of these VM’s based on an existing VM in my environment with an implementation date of June 19^th.

If we add this project to the forecast chart, the chart changes to accommodate this change in our environment:

By adding this project we have obviously created more demand, consequently, the date in which our disk space resources will exhaust has been expedited.

By having this knowledge we can plan our capacity requirements ahead of time. In this example, I decide to add another project to add resources prior to the commissioning of the aforementioned VM’s:

Because we can combine projects into a single chart, we can see based on observed metrics what effect adding demand and capacity to our environment has.

This is one of a vast number of features in vRealize Operations. vRealize Operations Manager can be an incredibly useful tool to have for a number of reasons. Its intelligent analytics, a breath of extensibility options and unified experience make it a compelling experience for modern cloud-based operations

GCP Kubernetes & VMware Wavefront – a practical demonstration

June 4, 2018 / David / 0 Comments

Wavefront

Back in 2017, VMware acquired Wavefront – a company based in the US which focuses predominantly on real-time metrics and monitoring of a really…really vast array of platforms and technologies. We have technologies that aid in adopting and promoting cloud-native implementations, but monitoring, in some peoples eyes, can be a bit of an afterthought. Wavefront to the rescue. Having developed some Kubernetes and Docker knowledge myself, it seemed rather fitting to get an example going.

GCP – Creating our Kubernetes cluster

To begin with, we need a Google Cloud project. Log into your GCP account and create one:

Access the Kubernetes Engine:

You may have to wait a few minutes for the Kubernetees engine to initialise. Once initialised, create a new Kubernetes cluster:

We have a number of options to define when we create a new Kubernetes cluster:

Note: You are not charged for, or responsible for deploying and maintaining the master nodes. As this is a hosted solution, Google takes care of this for us. As for the cluster options, we have the following base options to get us up and running, all of which should be pretty self-explanatory.

Name – The name for the cluster.
Description – Optional value.
Location – Determines whether our cluster’s master VMs are localised within a single zone or spread across multiple zones in one region.
Zone/Region – Determines where our clusters worker VM’s are localised.
Cluster Version – The version of Kubernetes to be deployed in this cluster.
Node Image – We have two choices, either Container-Optimised OS (cos) or Ubuntu.
Size – Number of nodes in our cluster

One aspect of this wizard I really like is the ability to extract the corresponding REST or CLI command to create the Kubernetes cluster based on the options selected:

Click “Create” to initialise the Kuberntes cluster.

GCP – Deploying a simple application

After waiting a few minutes our Kuberntes cluster has been created:

To connect to it, we can click the “Connect” button which will give us two options:

At this stage, you can deploy your own application, but for me, I deployed a simple application following the instructions located at https://cloud.google.com/kubernetes-engine/docs/tutorials/hello-app

Wavefront and Kubernetes integration

To get started, we need to deploy the following:

Wavefront Proxy
Wavefront Proxy Service
Heapster (Collector Agent)

The YAML files are located at the following URL : https://longboard.wavefront.com/integration/kubernetes/setup

Note that you’ll need a logon to access the above URL. Also, and very cleverly, the generated YAML files contain tokens specific to your account. Therefore, after deploying the YAML files Wavefront will automagically start collecting stats:

Thoughts on wavefront

Once I got everything up and running I was pretty much in awe of the sheer depth of what Wavefront has visibility of. From my tiny, insignificant environment I’m able to get extremely detailed metrics and content pertaining to:

Clusters
Namespaces
Nodes
Pods
Pod Containers

In particular, I was very impressed as to how easy it is to get wavefront to ingest data from the likes of GCP hosted K8s.