This is a continuation of an article that describes operational considerations for hosting solutions on Azure public cloud.

This articles focuses on support for containers and Kubernetes in Azure. 

Compute requirement of a modern cloud app typically involves load balanced compute nodes that operate together with control nodes and databases.

VM Scale sets provide scale, customization, availability, low cost and elasticity.

VM scale sets in Azure resource manager generally have a type and a capacity. App deployment allow VM extension updates just like OS updates.

Container infrastructure layering allows even more scale because it virtualizes the operating system. While traditional virtual machines enable hardware virtualization and hyper V’s allow isolation plus performance, containers are cheap and barely anything more than just applications.

Azure container service serves both linux and windows container services. It has standard docker tooling and API support with streamlined provisioning of DCOS and Docker swarm.

Azure is an open cloud because it supports open source infrastructure tools  such as  Linux, ubuntu, docker, etc. layered with databases and middleware such as hadoop, redis, mysql etc., app framework and tools such as nodejs, java, python etc., applications such as Joomla, drupal etc and management applications such as chef, puppet, etc. and finally with devops tools such as jenkins, Gradle, Xamarin etc.

Job based computations use larger sets of resources such as with compute pools that involve automatic scaling and regional coverage with automatic recovery of failed tasks and input/output handling.

Azure involves a lot of fine grained loosely coupled micro services using HTTP listener, Page content, authentication, usage analytic, order management, reporting, product inventory and customer databases.

 

Efficient Docker image deployment for intermittent low bandwidth connectivity scenarios requires the elimination of docker pulling of images. An alternative deployment mechanism can compensate for the restrictions by utilizing an Azure Container Registry, Signature Files, a fileshare, an IOT hub for pushing manifest to devices. The Deployment path involves pushing image to device which is containerized. The devices can send back messages which are collected in a device-image register. An image is a collection of layers where each layer represents a set of file-system differences and stored merely as folders and files. A SQL database can be used to track the state of what’s occurring on the target devices and the Azure based deployment services which helps with both during and after the deployment process.

 

Resource groups are created to group resources that share the same lifecycle. They have no bearing on the cost management of resources other than to help with querying. They can be used with tags to narrow down the interest. There is metadata stored about the resources and it is stored in a particular region. Resources can be moved from one resource group to another or even to another subscription. Finally, resource groups can be locked to prevent actions such as delete or write by users who have access.

As with its applicability to many deployments, Azure Monitor provides tremendous insight into operations of Azure Resources. It is always recommended to create multiple application insights resources and usually one per environment. This results in better separation of telemetry,alerts, workitems, configurations and permissions. Limits are spread such as web test count, throttling, data allowance etc and it also helps with cross-resource queries.