Archives For NIC

blue-abstract-glass-balls

Deploying Azure Marketplace VMs

The first step is to gather information about the Market Place Virtual Machine (VM) image that we want to deploy. For this example I decided to deploy a Tableau Server image.

Login-AzureRmAccount

$location = 'eastus'
  
Get-AzureRmVMImagePublisher -Location $location `
    | Where-Object -Property PublisherName -Like Tableau*
 
$publisherName = 'tableau'
  
Get-AzureRmVMImageOffer -Location $location `
                        -PublisherName $publisherName
 
$offer = 'tableau-server'
  
Get-AzureRmVMImageSku -Location $location `
                      -PublisherName $publisherName `
                      -Offer $offer `
      | Select-Object -Property 'Skus'

Skus                  
----                  
bring-your-own-license

Now that we have the image information, it’s time to create an Azure Resource Manager (ARM) Template. Continue Reading…

Ge0-HA-Map

Geo-HA Service Fabric Cluster

One of the biggest challenges that we face when we build an Internet-scale solution, is high availability across geographic locations (Geo-HA). Why is this important? Well, there can be a few different reasons. The most common reason, is to be able to survive data center outages. Another reason, is to bring services closer to end users so that we can provide good user experiences.

Geo-HA brings challenges to the table. For example, should we use an Active-Passive or Active-Active strategy for data across regions? Keeping in mind that Active-Active is difficult to get right, we need to take time to analyze and to make the correct choices. We need to consider our Disaster recovery (DR) plan, target RPO and RTO. Azure has a whole bunch of mechanisms for replication, backup and monitoring, so how do we decide what’s the right combination?

Today’s Internet-scale services are built using microservices. Service Fabric is a next-generation middleware platform used for building enterprise-class, Tier-1 services. This microservices platform allows us to build scalable, highly available, reliable, and easy to manage solutions. It addresses the significant challenges in developing and managing stateful services. The Reliable Actors API is one of two high-level frameworks provided by Service Fabric, and it is based on the Actor pattern. This API gives us an asynchronous, single-threaded programming model that simplifies our code while still providing the advantages of scalability and reliability guarantees offered by Service Fabric.

A Service Fabric cluster is HA within its geographic region by default. Thinking about our heritage of on premise data centers, we’ve poured thousands of man-hours to deploy Disaster Recovery sites in secondary physical locations, because we know that everything is possible. Over the past few years, we’ve experienced many interesting scenarios, for example, a cut cable, or a faulty DNS entry broke the Internet. So why should we do anything differently in the cloud? We must treat each region as we treat our own data centers and think about Geo-HA.

The rest of this post is about taking high availably to the next level by deploying a Geo-HA Service Fabric cluster. Continue Reading…

mind-the-gap

Based on the current builds, compared to Server, Nano Server has 93 percent lower VHD size, 92 percent fewer critical bulletins and 80 percent fewer reboots!

Deploying Nano Server to Azure

I’ve been curious about Nano Server for a while now. And I recently noticed that it was available on Microsoft Azure. This post is definitely from a developers point-of-view. It goes through the steps required to create a functional Nano Server Virtual Machines (VM) on Microsoft Azure.

Nano Server is ideal for many scenarios:

  • As a “compute” host for Hyper-V virtual machines, either in clusters or not
  • As a storage host for Scale-Out File Server.
  • As a DNS server
  • As a web server running Internet Information Services (IIS)
  • As a host for applications that are developed using cloud application patterns and run in a container or virtual machine guest operating system.

The Adventure

Nano Server is a remotely administered server operating system (OS). Wait. Let me repeat this because it’s important… Nano Server is a remotely administered server operating system (OS). Developers, Nano Server is a server OS optimized for clouds and data centers. It’s designed to take up far less disk space, to setup significantly faster, and to require far fewer restarts than Windows Server. So why does this matter? Well it means more resources, more availability and stability for our Apps. And it also means that it’s time to learn new skills, because there is no local logon capability at all, nor does it support Terminal Services. However, we have a wide variety of options for managing Nano Server remotely, including Windows PowerShell, Windows Management Instrumentation (WMI), Windows Remote Management, and Emergency Management Services (EMS). Continue Reading…

Traffic-Manager

Using ARM to Deploy Global Solutions

Imagine deploying your secure load balanced solution to three datacenters, putting in place a worldwide load balancer and doing so in roughly 24 minutes. Did I mention that this deployment is predictable and repeatable?

Good, now that I’ve got your attention, it’s time to dive in!

Building on my previous post about managing compute resources on Azure I decided to modify the Azure Resource Manager(ARM) template to deploy a real-world environment to three datacenters (Yes I know, the diagrams shows two locations, but as I built the demo, I got greedy…). Using Azure Traffic Manager we are able positively affect a users experience by directing them to the closest datacenter.

Its important to note that ARM does not support nested copy operation. This means that we have to use a different strategy to deploy identical environments in multiple Azure regions. After a bit of research it became apparent that I had to use nested deployments. This technique requires us to break our template into multiple files. The parent template in this demo is the azuredeploy-multi-geo.json file. It contains the full list of parameters, a nested deployment that deploys instances of our environment to multiple Azure regions, and a Traffic Manager definition. The azuredeploy.json template file was refactored from the template used in my previous blog post. It contains networking, storage and Virtual Machine definitions. Continue Reading…

multi-geo

While I was playing around with the Azure Resource Manager Copy Operation, I started thinking about what I could do with it. The first wild idea that popped into my head was, to use it to deploy multi-geo environments from a single ARM Template.

Alright, some of you might think that it’s not such great idea, and I can appreciate that. But I’m just too curious, so let’s give this a chance. Continue Reading…

ARM Resource Group

Deploying 20 CentOS VMs in 4 Minutes!

I recently started to toy around with scenarios that required me to deploy multiple duplicates of the same CentOS Virtual Machine configuration. Working on this scenario got me curious. So I decided to build a template that would allow me to deploy 20 CentOS Virtual Machines each with one 1TB data disk and one public IP addresses.

To my surprise, deploying these 20 Standard A1 CentOS Virtual Machines on Microsoft Azure took 4 minutes!

Building the ARM Template

Let’s start by taking a CentOS ARM Template from a previous post. It will be our starting point for this exercise. Now, let’s removed the extra data disk and removed the Custom Script for Linux Virtual Machine Extension.

To duplicate a resource, we must use the copy operation. It enables us to use an index number or to iterate through an array of values that can be used when deploying a resource.

"copy": {
          "name": "nodeCopy",
          "count": "[parameters('vmCount')]"
}

In this specific scenario, we want all our Virtual Machines to belong to the same Virtual Network and Subnet. Therefore, we need to duplicate each Virtual Machine, their Network Interface Cards (NIC) and their Public IP Addresses.

The following template, demonstrates the use of copyIndex() and concat(), to generate predictable identifiers for each copy. Continue Reading…