Last week, Microsoft announced the Preview of Capacity Reservation for VMs. You can reserve VM capacity in your DR region to ensure that you have VM resources available to create or turn on your protected VMs using ASR. ASR does not guarantee that your VMs can be turned on in your DR region in the event of disaster recovery. So, capacity reservation is a welcome feature and much needed. However, this is increasing the cost of your solution again.
ASR protected VM cost
Capacity Reservation Cost ( as same as your actual VM cost)
Note: DR is not just the VMs but including other components. I did not provide the details above because it applies to both the options.
Hmm.. Can we plan a DR cost-effectively in Azure? Let’s take a look:
The long waiting feature is now Public Preview. When there is a Azure region disaster recovery, we need our VMs to be turned on in the secondary region. This feature guarantees you the recovery. You may learn about this feature in this URL.
As you know already, Azure Site Recovery does not 100% guarantee to turn on your VMs in the event of DR. This feature addition helps you gaining more confidence with your BCP with this feature addition.
Take a look at the Microsoft statement about the use cases for this feature.
Business-critical applications—use on-demand capacity reservations to protect your capacity, for example when taking these VMs offline to perform updates.
Disaster recovery—you now have the option to set aside compute capacity to ensure a seamless recovery in the event of a natural disaster. The compute capacity can be repurposed to run other workloads whenever DR is not in effect. The VM maintenance can be handled by keeping core images up to date without the need to deploy or maintain VMs outside of DR testing.
Special events—claiming capacity ahead of time provides assurance that your business can handle the extra demand.
I have been asking for this feature for a long time now, and finally, it is here. I am happy about Microsoft as they are listening to customers and partners. However, the first bullet point is a bit worrisome as it states, It is not guaranteed to get your VM back if it is offline for some time due to maintenance. Does MSFT force customers to take this for all the critical workload? I hope they do not make things worse to this point.
Let me try bring some insight on Azure storage reservations. If you have not read my previous blog posts on VM reserved instance, please read it here. You can save on storage cost for blob data with Azure Storage reserved capacity. It covers both for block blobs and Data Lake Gen2. You must be owner of the subscription or EA admin if it is EA and you must a admin agent or sale agent to buy Azure Blob Storage reserved capacity. I tried to bring information from different MS article to one place with bullet points to help you on this.
Importantly, a reservation covers only the data stored in the subscription, it does not cover other actions like early deletion, operations, bandwidth and data transfer charges. You may note the below points when you think about the reserving the Azure storage.
Most of the organizations are keen on moving their workload to cloud today for several reasons like their IT vision, reduce the spend on hardware refreshes, data center consolidations etc.
Are they ready move into the Cloud? It is an important question that every organization should ask again and again before taking the decision to move in with big bang. We see a trend with many customers to move their existing legacy applications ‘as is’ to the cloud. Shouldn’t we move into the cloud and utilize those benefits, or we just move in and I don’t care about those cloud features?
Let me start with an example here. Let’s take a case of four webservers and two database servers clustered available 24/7 with environments like Dev, Test and Prod. And you wanted to move this workload to cloud ‘as is’. My question is, what is the objectives are you trying to achieve? If the answer is, our organization wanted to move all the workload to cloud for cost saving, changing from Capex to Opex model etc. Guys, hold on… Let’s calm down, think, look around and plan again.
Lift and shift should not be our strategy for cloud migration. We should make our application to live smartly in the cloud to utilize the cloud benefits and reduce the cost. Let’s use the above example to explore this further.
Can we make this application horizontally scalable?
Can we make this application to use cloud native authentication?
Can we make this application to work stateless?
Can we make the applications to use distributed data storage?
Yes, most of us work in Technology companies those advise many organizations globally for taking technological decision and encourage them to use the latest and advanced technologies to automate their operations. However, how many of those companies are using technology within their organizations and drive innovations for their internal requirements. Both the employees and employers are suffering because a proper system is not in place for solving the problems like Lack of effective appraisal system, identifying the right resources for the projects, unable to stop the talent leaving the organization etc. Let’s look at those and how we can try to solve using the technologies like Machine Learning(ML) and Artificial Intelligence(AI). Yes, we are in the world of Machine Learning(ML) and Artificial Intelligence (AI) and we must now start thinking about using them effectively.
Machine learning is ‘predictive analysis’ in very simple terms, agree? Arrive into a conclusion by analyzing the data. Can this possible only with computers? We, human beings do it on a daily to basis, to catch a bus, to drive a car, to shop and what not.
Don’t be confused. Let me try to explain and don’t blame me at the end if that doesn’t work, a pre-bail has been taken :-).
Let me take the example of how we learn driving. Excuse me for those who never tried it 🙂.
The driving instructor gives you first set of data like usage of steering wheel, gear shifting, clutch, brake, accelerator etc. Initially, with those basic data you mess with clutch, gear, accelerator and brake often. Then you slowly correct the mistakes by practicing which means you are learning by feeding your brain with additional data on how to use it effectively. Eventually you get it correct when you have more and more data; and create your own algorithm to drive your car. Result of this you start applying the break softer, start shifting the gear smoothly. That is exactly the machine learning does with the help of your intelligence (it is currently being replaced with Artificial Intelligence –AI 🙂). Continue reading “Machine Learning: I do it, you do it everyday without a computer”