Azure VM pricing - Is it better to have 80 single core machines or 10 8-core machines?

Question

I am limited by a piece of software that utilizes a single core per instance of the program run. It will run off an SQL server work queue and deposit results to the server. So the more instances I have running the faster the overall project is done. I have played with Azure VMs a bit and can speed up the process in two ways.

1) I can run the app on a single core VM, clone that VM and run it on as many as I feel necessary to speed up the job sufficiently.

OR

2) I can run the app 8 times on an 8-core VM, ...again clone that VM and run it on as many as I feel necessary to speed up the job sufficiently.

I have noticed in testing that the speed-up is roughly the same for adding 8 single core VMs and 1 8-core VM. Assuming this is true, would it better better price-wise to have single core machines?

The pricing is a bit of a mystery, whether it is real cpu usage time, or what. It is a bit easier using the 1 8-core approach as spinning up machines and taking them down takes time, but I guess that could be automated.

It does seem from some pricing pages that the multiple single core VM approach would cost less?

Side question: so could I do like some power shell scripts to just keep adding VMs of a certain image and running the app, and then start shutting them down once I get close to finishing? After generating the VMs would there be some way to kick off the app without having to remote in to each one and running it?

Answer 1

Billing

According to Windows Azure Virtual Machines Pricing Details, Virtual Machines are charged by the minute (of wall clock time). Prices are listed as hourly rates (60 minutes) and are billed based on total number of minutes when the VMs run for a partial hour.

In July 2013, 1 Small VM (1 virtual core) costs $0.09/hr; 8 Small VMs (8 virtual cores) cost $0.72/hr; 1 Extra Large VM (8 virtual cores) cost $0.72/hr (same as 8 Small VMs).

VM Sizes and Performance

The VMs sizes differ not only in number of cores and RAM, but also on network I/O performance, ranging from 100 Mbps for Small to 800 Mbps for Extra Large.

Extra Small VMs are rather limited in CPU and I/O power and are inadequate for workloads such as you described.

For single-threaded, I/O bound applications such as described in the question, an Extra Large VM could have an edge because of faster response times for each request.

It's also advisable to benchmark workloads running 2, 4 or more processes per core. For instance, 2 or 4 processes in a Small VM and 16, 32 or more processes in an Extra Large VM, to find the adequate balance between CPU and I/O loads (provided you don't use more RAM than is available).

Auto-scaling

Auto-scaling Virtual Machines is built-into Windows Azure directly. It can be based either on CPU load or Windows Azure Queues length.

Another alternative is to use specialized tools or services to monitor load across the servers and run PowerShell scripts to add or remove virtual machines as needed.

Auto-run

You can use the Windows Scheduler to automatically run tasks when Windows starts.

Answer 2

I would argue that all else being equal, and this code truly being CPU-bound and not benefitting from any memory sharing that running multiple processes on the same machine would provide, you should opt for the single core machines rather than multi-core machines.

Reasons:

Isolate fault domains

Scaling out rather than up is better to do when possible because it naturally isolates faults. If one of your small nodes crashes, that only affects one process. If a large node crashes, multiple processes go down.

Load balancing

Windows Azure, like any multi-tenant system, is a shared resource. This means you will likely be competing for CPU cycles with other workloads. Having small VMs gives you a better chance of having them distributed across physical servers in the datacenter that have the best resource situation at the time the machines are provisioned (you would want to make sure to stop and deallocate the VMs before starting them again to allow the Azure fabric placement algorithms to select the best hosts). If you used large VMs, it would be less likely to find a suitable host with optimal contention to accommodate many virtual cores.

Virtual processor scheduling

It's not widely understood how scheduling a virtual CPU is different than scheduling a physical one, but it is something worth reading up on. The main thing to remember is that hypervisors like VMware ESXi and Hyper-V (which runs Azure) schedule multiple virtual cores together rather than separately. So if you have an 8-core VM, the physical host must have 8 physical cores free simultaneously before it can allow the virtual CPU to run. The more virtual cores, the more unlikely the host will have sufficient physical cores at any given time (even if 7 physical cores are free, the VM cannot run). This can result in a paradoxical effect of causing the VM to perform worse as more virtual CPU cores are added to it. http://www.perfdynamics.com/Classes/Materials/BradyVirtual.pdf

In short, a single vCPU machine is more likely to get a share of the physical processor than an 8 vCPU machine, all else equal.

And I agree that the pricing is basically the same, except for a little more storage cost to store many small VMs versus fewer large ones. But storage in Azure is far less expensive than the compute, so likely doesn't tip any economic scale.

Hope that helps.

Answer 3

The pricing is "Uptime of the machine in hours * rate of the VM size/hour * number of instances"

e.g. You have a 8 Core VM (Extra Large) running for a month (30 Days) (30 * 24) * 0.72$ * 1= 518.4$

for 8 single cores it will be (30 * 24) * 0.09 * 8 = 518.4$

So I doubt if there will be any price difference. One advantage of using smaller machines and "scaling out" is when you have more granular control over scalability. An Extra-large machine will eat more idle dollars than 2-3 small machines.

Yes you can definitely script this. Assuming they are IaaS machines you could add the script to windows startup, if on PaaS you could use the "Startup Task". Reference