Category Public IP addresses

For this example, you will need a Windows VM set up in your subscription:

1. Navigate to the Azure portal at https://portal.azure.com.
2. In the search bar, type and select Virtual Machines and select the virtual machine you wish to apply Update Management to.
3. On the left-hand menu, click Guest + host updates under Operations.
4. Click the Go to Update Management button.
5. Complete the following details:
   a) Log Analytics workspace Location: The location of your VM, for example, East US
   b) Log Analytics workspace: Create default workspace
   c) Automation account subscription: Your subscription
   d) Automation account: Create a default account
6. Click Enable.

The process can take around 15 minutes once completed. Go back to the VM view and again select Guest + host updates under Operations, followed by Go to Update Management.

You will see a view similar to the following screenshot:

Figure 7.8 – Update Management blade

You can get to the same view but for all the VMs you wish to manage in the portal by searching for Automation Accounts and selecting the automation account that has been created. Then click Update management.

If you want to add more VMs, click the + Add Azure VMs button to see a list of VMs in your subscription and enable the agent on multiple machines simultaneously – as we see in the following screenshot:

Figure 7.9 – Adding more virtual machines for Update Management

The final step is to schedule the installation of patches:

1. Navigate to the Azure portal by opening https://portal.azure.com.
2. Type Automation into the search bar and select Automation Accounts.
3. Select the automation account.
4. Click Update Management.
5. Click Schedule deployment and complete the details as follows:
   a) Name: Patch Tuesday
   b) Operating System: Windows
   c) Maintenance Window (minutes): 120
   d) RebootReboot options:Reboot if required
6. Under Groups to update, click Click to Configure.
7. Select your subscription and Select All under Resource Groups.
8. Click Add, then OK.
9. Click Schedule Settings.
10. Set the following details:
    a) Start date: First Tuesday of the month
    b) Recurrence: Recurring
    c) Recur Every: 14 days
11. Click OK.
12. Click Create.

Through the Update Management feature, you can control how your virtual machines are patched and when and what updates to include or exclude. You can also set multiple schedules and group servers by resource group, location, or tag.

In the preceding example, we selected all VMs in our subscription, but as you saw, we had the option to choose a machine based on location, subscription, resource group, or tags.

In this way, you can create separate groups for a variety of purposes. For example, we mentioned earlier that a common practice would be to test patches before applying them to production servers. We can accommodate this by grouping non-production servers into a separate subscription, resource group, or simply tagging them. You can then create one patch group for your test machines, followed by another for production machines a week later – after you’ve had time to confirm the patches have not adversely affected workloads.

As part of any solution design that utilizes VMs, accommodation must be included to ensure they are always running healthily and securely, and Update Management is a critical part of this. As we have seen, Azure makes the task of managing OS updates easy and straightforward to set up.

Next, we will investigate another form of compute that is becoming increasingly popular – containerization and Kubernetes.

What to watch out for

Power Automate is only for simpler workflows and is not suitable when deeper or more advanced integration is required.

In this section, we have briefly looked at the many different compute technologies available in Azure. PaaS options are fully managed by the platform, allowing architects and developers to focus on the solution rather than management. However, when traditional IaaS compute options are required, such as virtual machines, security and OS patches must be managed yourself. Next, we will look at the native tooling that Azure provides to make this management easier.

Automating virtual machine management

Virtual machines are part of the IaaS family of components. One of the defining features of VMs in Azure is that you are responsible for keeping the OS up to date with the latest security patches.

In an on-premise environment, this could be achieved by manually configuring individual servers to apply updates as they become available; however, in many organizations, more control is required; such as, for example, the ability to have patches verified and approved before mass roll out to production systems, control when they happen, and control reboots when required.

Typically, this could be achieved using Windows Server Update Services (WSUS) and Configuration Manager, part of the Microsoft Endpoint Manager suite of products. However, these services require additional management and setup, which can be time-consuming.

As with most services, Azure helps simplify managing VM updates with a native Update Management service. Update Management uses several other Azure components, including the following:

• Log Analytics: Along with the Log Analytics agent, reports on the current status of patching for a VM
• PowerShell Desired State Configuration (DSC): Required for Linux patching
• Automation Hybrid Runbooks / Automation Account: Used to perform updates

Automation Account and Log Analytics workspaces are not supported together in all regions, and therefore you must plan when setting up Update Management. For example, if your Log Analytics workspace is in East US, your automation account must be created in East US 2.

See the following link for more details on region pairings: https://docs.microsoft.com/en-gb/azure/automation/how-to/region-mappings.

When setting up Update Management, you can either create the Log Analytics workspaces and automation accounts yourself or let the Azure portal make them for you. In the following example, we will select an Azure VM and have the portal set up Update Management.

Virtual machines offer a way to run multiple, isolated applications on a single piece of hardware. However, virtual machines are relatively inefficient in that every single instance contains a full copy of the operating system.

Containers wrap and isolate individual applications and their dependencies but still use the same underlying operating systems as other containers running on the host – as we can see in the following diagram:

Figure 7.4 – Containers versus virtual machines

This provides several advantages, including speed and the way they are defined. Azure uses Docker as the container engine, and Docker images are built-in code. This enables easier and repeatable deployments.

Because containers are also lightweight, they are much faster to provision and start up, enabling applications based on them to react quickly to demands for resources.

Containers are ideal for a range of scenarios. Many legacy applications can be containerized relatively quickly, making them a great option when migrating to the cloud.

Containers’ lightweight and resource-efficient nature also lends itself to microservice architectures whereby applications are broken into smaller services that can scale out with more instances in response to demand.

We cover containers in more detail later in this chapter, in the Architecting for containerization and Kubernetes section.

What to watch out for

Not all applications can be containerized, and containerization removes some controls that would otherwise be available on a standard virtual machine.

As the number of images and containers increases in an application, it can become challenging to maintain and manage them; in these cases, an orchestration layer may be required, which we will cover next.

Azure Kubernetes Service (AKS)

Microservice-based applications often require specific capabilities to be effective, such as automated provisioning and deployment, resource allocation, monitoring and responding to container health events, load balancing, traffic routing, and more.

Kubernetes is a service that provides these capabilities, which are often referred to as orchestration.

AKS stands for Azure Kubernetes Service and is the ideal choice for microservice-based applications that need to dynamically respond to events such as individual node outages or automatically scaling resources in response to demand. Because AKS is a managed service, much of the complexity of creating and managing the cluster is taken care of for you.

The following shows a high-level overview of a typical AKS cluster and it is described in more detail in the Azure Kubernetes Service section later in this chapter:

Figure 7.5 – AKS cluster

AKS is also platform-independent – any application built to run on the Kubernetes service can easily be migrated from one cluster to another regardless of whether it is in Azure, on-premise, or even another cloud vendor.

As already stated, we cover containers and AKS in more detail later in this chapter, in the Architecting for containerization and Kubernetes section.

Azure Batch is, of course, not suited to interactive applications such as websites or services that must store files locally for periods – although, as already discussed, it can output results to Azure Storage.

Service Fabric

Modern applications are often built or run as microservices, smaller components that can be scaled independently of other services. To achieve greater efficiency, it is common to run multiple services on the same VM. However, as an application will be built of numerous services, each of which needs to scale, managing, distributing, and scaling, known as orchestration, can become difficult.

Azure Service Fabric is a container orchestrator that makes the management and deployment of software packages onto scalable infrastructure easier.

The following diagram shows a typical Service Fabric architecture; applications are deployed to VMs or VM scale sets:

Figure 7.3 – Azure Service Fabric example architecture

It is particularly suited to .NET applications that would traditionally run on a virtual machine, and one of its most significant benefits is that it supports stateful services. Service Fabric powers many of Microsoft’s services, such as Azure SQL, Cosmos DB, Power BI, and others.

Tip

When building modern applications, there is often discussion around stateful and stateless applications. When a client is communicating with a backend system, such as a website, you need to keep track of those requests – for example, when you log in, how can you confirm the next request is from that same client? This is known as state. Stateless applications expect the client to track this information and provide it back to the server with every request – usually in the form of a token validated by the server. With stateful applications, the server keeps track of the client, but this requires the client to always to use the same backend server – which is more difficult when your systems are spread across multiple servers.

Using Service Fabric enables developers to build distributed systems without worrying about how those systems scale and communicate. It is an excellent choice for moving existing applications into a scalable environment without the need to completely re-architect.

What to watch out for

You will soon see that there are many similarities between Service Fabric and AKS clusters – one of the most significant differences between the two is portability. Because Service Fabric is tightly integrated into Azure and other Microsoft technologies, it may not work well if you need to move the solution to another platform.

Each type of compute has its own set of strengths; however, each also has its primary use cases, and therefore, might not be suitable for some scenarios.

Virtual machines

As the closest technology to existing on-premise systems, VMs are best placed for use cases requiring either fast migration to the cloud or those legacy systems that cannot run on other services without reworking the application.

The ability to quickly provision, test, and destroy a VM makes them ideal for testing and developing products, especially when you need to ascertain how a particular piece of software works on different operating systems.

Sometimes a solution may have stringent requirements around security in that they cannot use shared compute. Running such applications on VMs helps ensure processing is not shared. Through the use of dedicated hosts, you can even provision your physical hardware to run those VMs on.

What to watch out for

To make VMs scalable and resilient, you must architect and deploy supporting technologies or configure the machines accordingly. By default, a single VM is not resilient. Failure of the physical hardware can disrupt services, and the servers do not scale automatically.

Building multiple VMs in availability sets and across Availability Zones can protect you against many such events, and scale sets allow you to configure automatic scaling. However, these are optional configurations and may require additional components such as load balancers. These options require careful planning and can increase costs.

Important note

We will cover availability sets and scale sets in more detail in Chapter 14, High Availability and Redundancy Concepts.

Azure Batch

With Azure Batch, you create applications that perform specific tasks, which run in node pools. Node pools can contain thousands of VMs that are created, run a task, and are then decommissioned. No information is stored on the VMs themselves. However, the input and output of datasets can be achieved by reading and writing to Azure storage accounts.

Azure Batch is suited to the parallel processing of tasks and high-performance computing (HPC) batch jobs. Being able to provision thousands of VMs for short periods, combined with per-second billing, ensures efficient costs for such projects.

The following diagram shows how a typical batch service might work. As we can see, input files can be ingested from Azure Storage by the Batch service, which then distributes it to a node in a node pool for processing. The code that performs the processing is held within Azure Batch as a ZIP file. All output is then sent back out to the storage account:

Figure 7.2 – Pool, job, and task management

Some examples of a typical workload may include the following:

• Financial risk modeling
• Image and video rendering
• Media transcoding
• Large data imports and transformation

With Azure Batch, you can also opt for low-priority VMs – these are cheaper but do not have guaranteed availability. Instead, they are allocated from surplus capacity within the data center. In other words, you must wait for the surplus compute to become available.

• Scalability

Different services have different methods for scaling. Legacy applications may need to use traditional load balancing methods by building VMs in web farms with load balancers in front to distribute the load.

Modern web applications can make use of App Service or Azure Functions, which scale automatically without the need for additional components.

• Availability

Each Azure service has a Service-Level Agreement (SLA) that determines a baseline for how much uptime a service offers. The mix of components used can also affect this value. For example, a single VM has an SLA of 95%, whereas two VMs across Availability Zones with a load balancer in front has an SLA of 99.99%.

Azure Functions and App Service have an SLA of 99.95% without any additional components.

Important note

Service-Level Agreements (SLAs) define specific metrics by which a service is measured. In Azure, it is the amount of time any particular service is agreed to be available for. This is usually measured as a percentage of that uptime – for example, 99.95% (referred to as three and a half nines) or 99.99% (referred to as four nines). Your choice of components and how they are architected will impact the SLA Microsoft offers.

An SLA of 99.95% means up to 4.38 hours of downtime a year is allowed, whereas 99.99% means only 52.60 minutes are permitted.

• Security

As services move from IaaS to PaaS and FaaS, the security responsibility shifts. For VMs, Microsoft is responsible for the physical security and underlying infrastructure, whereas you are responsible for patching, anti-virus software, and applications that run on them. For PaaS and FaaS, Microsoft is also responsible for security on the service. However, you need to be careful of different configuration elements within the service that may not be compliant with your requirements.

For some organizations, all traffic flow needs to be tightly controlled, especially for internal services; most PaaS solutions support this but only as a configurable option, which can sometimes increase costs.

• Cost

FaaS provides a very granular cost model in that you pay for execution time. Whereas IaaS and some PaaS demand you provision set resources based on required CPU and RAM. For example, a VM incurs costs as long as it is running, which is continual for many use cases.

When migrating existing legacy applications, this may be the only option, but it isn’t the most efficient from a cost perspective. Refactoring applications may cost more upfront but could be cheaper in the long run as they only consume resources and incur costs periodically.

Similarly, a new microservice built to respond to events on an ad hoc basis would suit an Azure function, whereas the same process running on a VM would not be cost-effective.

• Architecture styles

How an application is designed can directly impact the choice of technology. VMs are best suited to older architectures such as N-tier, whereas microservice and event-driven patterns are well suited to Azure Functions or containerization.

• User skills

Azure provides several technologies for no-code development. Power Automate, and the workflow development system, is specifically built to allow end users with no development knowledge to quickly create simple apps.

As you can see, to decide on a compute technology, you must factor in many different requirements. The following chart shows a simple workflow to help in this process:

Figure 7.1 – Compute options workflow

Next, we will look in more detail at each service and provide example use cases.

In the previous chapter, we looked at how to secure our Azure applications using key vaults, security principals, and managed identities.

When building solutions in Azure many components use some form of compute – such as a virtual machine (VM). However, there are many different types of compute, each with its own strengths. Therefore, in this chapter, we focus on the different types of compute services we have available to us and which options are best suited to which scenarios.

We will then maintain the security and health of VMs by ensuring they are always up to date with the latest OS patches.

Finally, we’ll look at containerization and how we can use Azure Kubernetes Service (AKS).

With this in mind, we will be covering the following topics:

• Understanding different types of compute
• Automating virtual machine management
• Architecting for containerization and Kubernetes

Technical requirements

This chapter will use the Azure portal (https://portal.azure.com) for examples.

Understanding different types of compute

When we architect solutions, there will often be at least one component that needs to host, or run, an application. The application could be built specifically for the task or an off-the-shelf package bought from a vendor.

Azure provides several compute services for hosting your application; each type can be grouped into one of three kinds of hosting model:

• Infrastructure as a Service (IaaS): VMs are within this category and support services such as storage (that is, disk drives) and networking. IaaS is the closest to a traditional on-premise environment, except Microsoft manages the underlying infrastructure, including hardware and the host operating system. You are still responsible for maintaining the guest operating system, however, including patching, monitoring, anti-virus software, and so on.
• Platform as a Service (PaaS): Azure App Service is an example of a PaaS component. With PaaS, you do not need to worry about the operating system (other than to ensure what you deploy to it is compatible). Microsoft manages all maintenance, patching, and anti-virus software; you simply deploy your applications to it. When provisioning PaaS components, you generally specify an amount and CPU and RAM, and your costs will be based on this.
• Serverless or Function as a Service (FaaS): FaaS, or serverless, is at the opposite end to IaaS. With FaaS, any notion of CPU, RAM, or management is completely abstracted away; you simply deploy your code, and the required resources are utilized to perform the task. Because of this, FaaS pricing models are calculated on exact usage, for example, the number of executions, as opposed to IaaS, where pricing is based on the specific RAM and CPU.

Some services may appear to blur the line between the hosting options; for example, VMs can be built as scale sets that automatically scale up and down on demand.

Generally, as you move from IaaS to FaaS, management becomes easier; however, control, flexibility, and portability are lost.

When choosing a compute hosting model for your solution, you will need to consider many factors:

• Deployment and compatibility

Not all applications can run on all services without modification. Older applications may have dependencies on installed services or can only be deployed via traditionally installed packages. For these legacy systems, an IaaS approach might be the only option.

Conversely, a modern application built using Agile DevOps processes, with regularly updated and redeployed components, might be better suited to Web Apps or Azure Functions.

• Support

Existing enterprise systems typically have support teams and processes embedded within the organization and will be used to patch and update systems in line with existing support processes.

Smaller companies may have fewer IT resources to provide these support tasks. Therefore, they would benefit significantly from PaaS or FaaS systems that do not require maintenance as the Azure platform handles this.