Skip to main content

Scale Units and Cloud

In this post we will talk about what is a scale unit and what are the benefits of scale unit concept when we are working with a system that is running in a cloud environment.
What is a scale unit?
We can see a scale unit as a group of resources that are grouped together to serve a specific number of clients or requests.  This scale unit has a ‘common’ configuration that specifies the resources that are needed by a scale unit.
Let’s assume that we have a scale unit that contains:

  • 2 Azure SQL
  • 4 Service Bus Namespaces (with 100 Queues per namespace)
  • 8 Worker Roles
  • 3 Web Roles
  • 2 Different storage accounts

Having all of them grouped together we can test the environment at a specific scale. Otherwise we could try to scale our system infinitely, but all of us knows that this is not possible. All the resources under the same scale unit work together for the same purpose.
Each scale unit serve a specific number of clients (or resources). Because the scale unit is fixed we can know exactly what is the throughput of our scale unit - number of requests per second, number of messages that can be consumed, number of access at storage and so on.
In the end we will know exactly the number of clients that we can server or manage for each scale unit and the cost of a scale unit.
Scaling can be made easily without affecting the performance of the system, by adding new units each time.
This means that we scale very easily, by adding new scale units. For each scale unit we know exactly what are the costs. In this way we can estimate the cost easily.
The hardest thing is to separate all each scale units. Between each scale unit we should not have any kind of communication or a central node (a master one). This is the hardest thing to accomplish, because large systems are very complex with a lot of dependencies.
I think that scale unit can help us to be able to predict the necessary request and to scale in a safe way.


In the above example we can see to instances of our scale unit. Each scale unit is mapped to a specific scale unit. There is no communication between scale unit. Each scale unit can be hosted in the same data centers or in different data centers, based on our needs.

In the future, with the new portal. we will be able very easily to create the provisioning for a scale unit and control the provisioning with a few clicks. Azure V2 will allow us to define a JSON file that can be used to provision all the components from our scale unit and connected between them, without having to specify the storage account name and key to the worker roles that need this information (we will be able to do this using a script).

In the next post we will try to see how we map a system that requires 'some' communication between scale units.

Comments

Popular posts from this blog

Windows Docker Containers can make WIN32 API calls, use COM and ASP.NET WebForms

After the last post , I received two interesting questions related to Docker and Windows. People were interested if we do Win32 API calls from a Docker container and if there is support for COM. WIN32 Support To test calls to WIN32 API, let’s try to populate SYSTEM_INFO class. [StructLayout(LayoutKind.Sequential)] public struct SYSTEM_INFO { public uint dwOemId; public uint dwPageSize; public uint lpMinimumApplicationAddress; public uint lpMaximumApplicationAddress; public uint dwActiveProcessorMask; public uint dwNumberOfProcessors; public uint dwProcessorType; public uint dwAllocationGranularity; public uint dwProcessorLevel; public uint dwProcessorRevision; } ... [DllImport("kernel32")] static extern void GetSystemInfo(ref SYSTEM_INFO pSI); ... SYSTEM_INFO pSI = new SYSTEM_INFO(

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

What to do when you hit the throughput limits of Azure Storage (Blobs)

In this post we will talk about how we can detect when we hit a throughput limit of Azure Storage and what we can do in that moment. Context If we take a look on Scalability Targets of Azure Storage ( https://azure.microsoft.com/en-us/documentation/articles/storage-scalability-targets/ ) we will observe that the limits are prety high. But, based on our business logic we can end up at this limits. If you create a system that is hitted by a high number of device, you can hit easily the total number of requests rate that can be done on a Storage Account. This limits on Azure is 20.000 IOPS (entities or messages per second) where (and this is very important) the size of the request is 1KB. Normally, if you make a load tests where 20.000 clients will hit different blobs storages from the same Azure Storage Account, this limits can be reached. How we can detect this problem? From client, we can detect that this limits was reached based on the HTTP error code that is returned by HTTP