“You can’t predict a disaster, but you can be prepared for one!” Disaster recovery is one of the biggest challenges for infrastructure. Amazon Web Services allows us to easily tackle this challenge and ensure business continuity. In this post, we’ll take a look at what disaster recovery means, compare traditional disaster recovery versus that in the cloud, and explore essential AWS services for your disaster recovery plan.

What is Disaster Recovery?

There are several disaster scenarios that can impact your infrastructure. These include natural disasters such as an earthquake or fire, as well as those caused by human error such as unauthorized access to data, or malicious attacks.

“Any event that has a negative impact on a company’s business continuity or finances could be termed a disaster.”

In any case, it is crucial to have a tested disaster recovery plan ready. A disaster recovery plan will ensure that our application stays online no matter the circumstances. Ideally, it ensures that users will experience zero, or at worst, minimal issues while using your application.

If we’re talking about on-premise centers, a disaster recovery plan is expensive to maintain and implement. Often, such plans are insufficiently tested or poorly documented. As such, it’s adequate for protecting resources. More often than not, companies with a good disaster recovery plan aren’t capable of conducting it because it was never tested in a real environment. As a result, users cannot access the application and the company suffers significant losses.

Let’s take a closer look at some of the important terminology associated with disaster recovery:

Business Continuity. All of our applications require Business Continuity. Business Continuity ensures that an organization’s critical business functions continue to operate or recover quickly despite serious incidents.

Disaster Recovery. Disaster Recovery (DR) enables recovery or continuation of vital technology infrastructure and systems following a natural or human-induced disaster.

RPO and RTO. Recover Point Objective (RPO) and Recovery Time Objective (RTO) are the two most important parts of a good DR plan for our workflow. Recover Point Objective (RPO) is the maximum targeted period in which data might be lost from an IT service due to a major incident. Recovery Time Objective (RTO) is a targeted time period after which a business process must be restored after a disaster or disruption to service.

Recovery Point Objective (RPO) and Recovery Time Objective (RTO)

Traditional Disaster Recovery plan (on-premise)

A traditional on-premise Disaster Recovery plan often includes a fully duplicated infrastructure that is physically separate from the infrastructure that contains our production. In this case, an additional financial investment is required to cover expenses related to hardware and for maintenance and testing. When it comes to on-premise data centers, physical access to the infrastructure is often overlooked.

These are the security requirements for an on-premise data center disaster recovery infrastructure:

• Facilities to house the infrastructure, including power and cooling.
• Security to ensure the physical protection of assets.
• Suitable capacity to scale the environment.
• Support for repairing, replacing, and refreshing the infrastructure.
• Contractual agreements with an internet service provider (ISP) to provide internet connectivity that can sustain bandwidth utilization for the environment under a full load.
• Network infrastructure such as firewalls, routers, switches, and load balancers.
• Enough server capacity to run all mission-critical services. This includes storage appliances for the supporting data, and servers to run applications and backend services such as user authentication, Domain Name System (DNS), Dynamic Host Configuration Protocol (DHCP), monitoring, and alerting.

Obviously, this kind of disaster recovery plan requires large investments in building disaster recovery sites or data centers (CAPEX). In addition, storage, backup, archival and retrieval tools, and processes (OPEX) are also expensive. And, all of these processes, especially installing new equipment, take time.

An on-premise disaster recovery plan can be challenging to document, test, and verify, especially if you have multiple clients on a single infrastructure. In this scenario, all clients on this infrastructure will experience problems with performance even if only one client’s data is corrupted.

Disaster Recovery plan on AWS

There are many advantages of implementing a disaster recovery plan on AWS.

Financially, we will only need to invest a small amount in advance (CAPEX), and we won’t have to worry about the physical expenses for resources (for example, hardware delivery) that we would have in on an “on-premise” data center.

AWS enables high flexibility, as we don’t need to perform a failover of the entire site in case only one part of our application isn’t working properly. Scaling is fast and easy. Most importantly, AWS allows a “pay as you use” (OPEX) model, so we don’t have to spend a lot in advance.

Also, AWS services allow us to fully automate our disaster recovery plan. This results in much easier testing, maintenance, and documentation of the DR plan itself.

This table shows the AWS service equivalents to an infrastructure inside an on-premise data center.

Essential AWS Services for Disaster Recovery

While planning and preparing a DR plan, we’ll need to think about the AWS services we can use. Also, we need to understand our selected services support data migration and durable storage. These are some of the key features and services that you should consider when creating your Disaster Recovery plan:

AWS Regions and Availability Zones  –  The AWS Cloud infrastructure is built around Regions and Availability Zones (“AZs”). A Region is a physical location in the world that has multiple Availability Zones. Availability Zones consist of one or more discrete data centers, each with redundant power, networking, and connectivity housed in separate facilities. These AZs allow you to operate production applications and databases that are more highly available, fault tolerant, and scalable than would be possible from a single data center.

Amazon S3 – Provides a highly durable storage infrastructure designed for mission-critical and primary data storage. Objects are redundantly stored on multiple devices across multiple facilities within a region and are designed to provide a durability of 99.999999999% (11 9s).

Amazon Glacier – Provides extremely low-cost storage for data archiving and backup. Objects are optimized for infrequent access, for which retrieval times of several hours are adequate.

Amazon EBS –  Provides the ability to create point-in-time snapshots of data volumes. You can use the snapshots as the starting point for new Amazon EBS volumes. And, you can protect your data for long-term durability because snapshots are stored within Amazon S3.

AWS Import/Export – Accelerates moving large amounts of data into and out of AWS by using portable storage devices for transport. The AWS Import/Export service bypasses the internet and transfers your data directly onto and off of storage devices using Amazon’s high-speed internal network.

AWS Storage Gateway is a service that connects an on-premise software appliance with cloud-based storage. This provides seamless, highly secure integration between your on-premise IT environment and the AWS storage infrastructure.

Amazon EC2 – Provides resizable compute capacity in the cloud. In the context of DR, the ability to rapidly create virtual machines that you can control is critical.

Amazon EC2 VM Import Connector enables you to import virtual machine images from your existing environment to Amazon EC2 instances.

Amazon Route 53 is a highly available and scalable Domain Name System (DNS) web service.

Elastic Load Balancing automatically distributes incoming application traffic across multiple Amazon EC2 instances.

Amazon VPC allows you to provision a private, isolated section of the AWS cloud. Here,  you can launch AWS resources in a virtual network that you define.

Amazon Direct Connect makes it easy to set up a dedicated network connection from your premises to AWS.

Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud.

AWS CloudFormation gives developers and systems administrators an easy way to create a collection of related AWS resources and provision them in an orderly and predictable fashion. You can create templates for your environments and deploy associated collections of resources (called a stack) as needed.

Disaster Recovery Scenarios with AWS

There are several strategies that we can use for disaster recovery of our on-premise data center using AWS infrastructure:

• Backup and Restore
• Pilot Light
• Warm Standby
• Multi-Site

Backup and Restore

The Backup and Restore scenario is an entry level form of disaster recovery on AWS. This approach is the most suitable one in the event that you don’t have a DR plan.

In on-premise data centers, data backup would be stored on tape. Obviously it will take time to recover data from tapes in the event of a disaster. For Backup and Restore scenarios using AWS services, we can store our data on Amazon S3 storage, making them immediately available if a disaster occurs. If we have a large amount of data that needs to be stored on Amazon S3, ideally we would use AWS Export/Import or even AWS Snowball to store our data on S3 as soon as possible.

AWS Storage Gateway enables snapshots of your on-premise data volumes to be transparently copied into Amazon S3 for backup. You can subsequently create local volumes or Amazon EBS volumes from these snapshots.

Backup and Restore scenario

The Backup and Restore plan is suitable for lower level business-critical applications. This is also an extremely cost-effective scenario and one that is most often used when we need backup storage. If we use a compression and de-duplication tool, we can further decrease our expenses here. For this scenario, RTO will be as long as it takes to bring up infrastructure and restore the system from backups. RPO will be the time since the last backup.

Pilot Light

The term “Pilot Light” is often used to describe a DR scenario where a minimal version of an environment is always running in the cloud. This scenario is similar to a Backup and Restore scenario. For example, with AWS you can maintain a Pilot Light by configuring and running the most critical core elements of your system in AWS. When the time comes for recovery, you can rapidly provision a full-scale production environment around the critical core.

Pilot Light scenario

A Pilot Light scenario is suitable for solutions that require a lower RTO and RPO. This scenario is a mid-range cost DR solution.

Warm Standby

A Warm Standby scenario is an expansion of the Pilot Light scenario where some services are always up and running. As we plan a DR plan, we need to identify crucial points of our on-premise infrastructure and then duplicate it inside the AWS. In most cases, we’re talking about web and app servers running on a minimum-sized fleet. Once a disaster occurs, infrastructure located on AWS takes over the traffic and performs its scaling and converting to a fully functional production environment with minimal RPO and RTO.

Warm standby scenario

The Warm Standby scenario is more expensive than Backup and Restore and Pilot Light because in this case, our infrastructure is up and running on AWS. This is a suitable solution for core business-critical functions and in cases where RTO and RPO need to be measured in minutes.

Multi-Site

The Multi-Site scenario is a solution for an infrastructure that is up and running completely on AWS as well as on an “on-premise” data center. By using the weighted route policy on Amazon Route 53 DNS, part of the traffic is redirected to the AWS infrastructure, while the other part is redirected to the on-premise infrastructure.

Data is replicated or mirrored to the AWS infrastructure.

Multi-Site scenario

In a disaster event, all traffic will be redirected to the AWS infrastructure. This scenario is also the most expensive option, and it presents the last step toward full migration to an AWS infrastructure. Here, RTO and RPO are very low, and this scenario is intended for critical applications that demand minimal or no downtime.

Wrap up

There are many options and scenarios for Disaster Recovery planning on AWS.

The scope of possibilities has been expanded further with AWS’ announcement of its strategic partnership with VMware. Thanks to this partnership, users can expand their on-premise infrastructure (virtualized using VMware tools) to AWS, and create a DR plan via resources provided by AWS using VMware tools that they are already accustomed to using.

Don’t allow any kind of disaster to take you by surprise. Be proactive and create the DR plan that best suits your needs.

If you’re new to the field, you will want to choose the platform that will help you get started with cloud computing. As a longtime AWS user, I believe that this is an excellent platform for a future cloud user. But there are also valid reasons for being familiar with all of the leading cloud providers. This post is about AWS vs Microsoft Azure and Google Cloud with a focus on the following categories: Compute, analytics, storage, network, and pricing.

AWS vs Microsoft Azure and Google Cloud Platform

First, let’s say a few words about each of the platforms:

• Amazon Web Services. Launched in 2006, AWS has a bit of a head start on the other platforms. With constant innovations and improvements over the years, the platform now has more than 70 services with a wide range of coverage. AWS servers are available in 14 geographical regions. Market share of the company is steadily growing, reporting 31% market share in the second quarter of 2016.
• Microsoft Azure. Running since 2010, Microsoft Azure is a complex system that provides support for many different services, programming languages, and frameworks. It has 67 services and data centers in 30 different geographical regions. It currently holds 11% of the market as of Q2 2016.
• Google Cloud Platform. Introduced in 2011, Google Cloud Platform is the youngest platform. Designed to meet the needs of Google Search and Youtube, it became available to everyone as a part of the Google for Work package. It has more than 50 services and 6 global data centers, with another 8 announced for 2017. With only 5% of market share and quite aggressive expansion, Google’s moment is yet to come.

Now that we know who are we dealing with, let’s start with our comparison:

Compute

Computing is a fundamental process for your entire business. The advantage of cloud computing is that you have a powerful and expandable computing force at your disposal that is ready when you need it.

The central AWS computing service is Elastic Compute Cloud (EC2). EC2 has become a synonym for scalable computing on demand. Depending on the industry, additions such as AWS Elastic Beanstalk or EC2 Container Services can significantly reduce your costs. At the moment, AWS supports 7 different instance families and 38 instance types. It also offers regional support and zone support at the same time.

The heart of Microsoft Azure computing is Virtual Machines and Virtual Machine Scale Sets, which can be used for processing. Windows client apps can be deployed with the RemoteApp service. Using Azure, you can use 4 different instance families, 33 instance types, and you can place it in different regions. Zone support is not provided.

Google Cloud Platform uses Compute Engine for running computing processes. One disadvantage is that its pricing is less flexible compared to AWS and Azure. It supports most of the main services that you would need such as container deployment, scalability, web and mobile apps processing, etc. Google Cloud supports 4 instance families, 18 different instance types, and provides regional and zone support.

AWS is the clear front runner when it comes to compute power. Not just because it offers you the most learning resources, but also because it provides the best learning platform.

Analytics

Cloud computing platforms provide quite a lot of useful data about your business. All you need to do to is make the proper analysis.

In the field of data analytics, AWS has made an entry to a big data and machine learning. However, if you don’t need extensive data analysis, you can use its Quick Sight service. This service will help you discover patterns and make correct conclusions from the data you’re receiving.

Similarly, Azure has taken steps toward big data and machine learning, but they don’t have a specific offering in these areas.

Google Cloud Platform, however, has the most advanced offering for big data analysis, machine learning, and artificial intelligence.

If you’re looking for a high level of data analytics, Google Cloud Platform is probably the best choice. However, if you just want to keep track of your daily business, AWS will serve you just fine.

Storage

Storage is an important pillar of cloud computing because it enables us to allocate all sorts of information (needed for our business) in an online location.

The AWS Simple Storage Service, known as S3, is pretty much industry standard. As a result, you will find a wealth of documentation, case studies, webinars, sample codes, libraries, and tutorials to consult, as well as forum discussions where AWS engineers participated. It’s also good to know that S3 is object oriented storage and you can also use Glacier as the archiving service.

Azure and Google Cloud Platform both have quite reliable and robust storage, but you won’t find anywhere near as much documentation and information about them as you will with AWS. They also have working and archive storage and different additional services, but they can’t out-perform AWS.

Here, AWS’s deep resources for new users makes it the clear champion in this category.

Network

It may come in handy to have your network in the cloud. You can have your VPN in an isolated place for your team only. And, it’s a great feature that adds value to your cloud system.

The AWS offering here is quite good. You can use the Virtual Private Cloud to create your VPN and set your network topology, create subnets, route tables, even private IP address ranges, and network gateways. On top of that, you can use Route 53 to have your DNS web service.

Microsoft Azure also has a solid private networking offer. Its Virtual Network (VNET) allows you to set your VPN, have public IP if you want, and use a hybrid cloud, firewall, or DNS.

Google Cloud Platform’s offering is not as extensive. It has the Cloud Virtual Network, and supports subnet, Public IP, firewall protection, and DNS.

The networking category winner is AWS because it has the most reliable DNS provider.

Pricing

At the end of the day, everyone wants to know: “So, how much is that going to cost me?” Because prices for each provider will be formed according to your needs and requirements we can’t quote exact costs here. However, we can tell you about the pricing models that each provider is using.

AWS uses three payment models:

• On demand: You pay only for the resources and services you use
• Reserve: Choose the quantity of resources that you want to book upfront for 1 to 3 years and pay based on utilization
• Spot: Take advantage of unused capacity and bid with others for additional space

Please note that AWS charges are rounded by the hour used.

Azure pricing is a bit flexible and charges per minute, by rounding per commitments. Their pricing models aren’t as flexibile compared to other platforms. Sustained use pricing is created to enable discounts in the case of on-demand use if a particular instance is used for a larger percentage of the month.

GCP pricing is similar to Azure. They also charge per minute, rounding in 10 minutes per period. In addition to on-demand charging, GCP offers sustained use discounting, which means that you will get a discount for regular usage.

Pricing models are a bit tricky. Each platform offers a pricing calculator that can help you estimate costs. If you consider using AWS, I would suggest that you get in touch with a local APN company, and they can help you estimate your monthly costs.

Vid Microsoft has entered the virtual reality race, announcing a new headset called Evo in collaboration with Intel.

The headset will have the same advanced features of current high-end products including the Oculus Rift, HTC Vive and soon-to-be-launched Sulon Q, but will work with mid-range laptops, the company said.

Up to now, Microsoft has focused its VR efforts on augmented reality (AR) and its Hololens glasses that add digital elements on a screen that you look through to the real world. The Evo will go full VR, covering your eyes and then reflecting an augmented reality back to you.

Critically, the Evo will allow for “inside-out” spacial awareness, meaning that sensors will be built into the headset to allow you to walk around a physical space with the headset, rather than requiring that external sensors be set up within your room to define a space.

That inside-out technology is what Sulon Q hopes to help it get first maneuver advantage on the market when it launches early next year, while both the Rift and Vive are furiously working on their own versions.

Promotional videos for Microsoft’s new Evo also show the headset as being wireless – again, something that Sulon Q is pushing as a unique advantage to its system (it has a full Windows 10 computer built into the headset), and something that both Oculus and Vive are working on.

At the moment, high-end headsets have to be physically connected by a wire to a high-spec computer. The Evo, on the other hand, will be wirelessly paired with a computer to achieve, well, this:

Youtube Video

Hm, what does this remind us of? Oh yes, that’s right. Microsoft Bob. Which it tried to make a success in 2015. And failed. It even has the little dog in the corner, too. Maybe 2017 will be kinder.

Youtube Video

Back to 2016: if Microsoft announced a new VR headset that wasn’t wireless and didn’t have inside-out tracking, it would have been laughed out of the room. The big question is when will it launch?

And on that Microsoft is being wildly vague. It says its Hololens should be available “in the first half of 2017,” and it says it has already shared the specs for PCs that will power its new headset, with those PCs available “next year.” It says developer kits will be made available to developers at the Game Developers Conference in San Francisco in February.

And it announced that the hardware developer 3Glasses will “bring the Windows 10 experience to their S1 device in the first half of 2017” – but that’s not the same as saying Microsoft Evo headsets will be available by then.

Incidentally the minimum specs for the new headsets are:

• Intel Mobile Core i5 dual-core
• Intel HD Graphics 620 (GT2) or equivalent
• 8GB RAM
• HMDI 1.4 or 2
• 100GB drive (preferably solid state)
• Bluetooth 4.0

Taking all the announcements together, it looks as though Microsoft is aiming at a Q3 or Q4 2017 launch of its VR headset – a timeline that is likely to give Oculus, Vive and Sulon a few months’ head start, but probably not enough of one to steal the market.

Where Microsoft and Intel really could win, however, is if they do manage to create a good VR system that requires a less powerful machine to run. That would pull down the price tag for the whole system and place it above the current best offering on the market – the PlayStation VR – in terms of quality. ®

Sponsored:
Magic quadrant for enterprise mobility management suites

Vid Microsoft has entered the virtual reality race, announcing a new headset called Evo in collaboration with Intel.

The headset will have the same advanced features of current high-end products including the Oculus Rift, HTC Vive and soon-to-be-launched Sulon Q, but will work with mid-range laptops, the company said.

Up to now, Microsoft has focused its VR efforts on augmented reality (AR) and its Hololens glasses that add digital elements on a screen that you look through to the real world. The Evo will go full VR, covering your eyes and then reflecting an augmented reality back to you.

Critically, the Evo will allow for “inside-out” spacial awareness, meaning that sensors will be built into the headset to allow you to walk around a physical space with the headset, rather than requiring that external sensors be set up within your room to define a space.

That inside-out technology is what Sulon Q hopes to help it get first maneuver advantage on the market when it launches early next year, while both the Rift and Vive are furiously working on their own versions.

Promotional videos for Microsoft’s new Evo also show the headset as being wireless – again, something that Sulon Q is pushing as a unique advantage to its system (it has a full Windows 10 computer built into the headset), and something that both Oculus and Vive are working on.

At the moment, high-end headsets have to be physically connected by a wire to a high-spec computer. The Evo, on the other hand, will be wirelessly paired with a computer to achieve, well, this:

Youtube Video

Hm, what does this remind us of? Oh yes, that’s right. Microsoft Bob. Which it tried to make a success in 2015. And failed. It even has the little dog in the corner, too. Maybe 2017 will be kinder.

Youtube Video

Back to 2016: if Microsoft announced a new VR headset that wasn’t wireless and didn’t have inside-out tracking, it would have been laughed out of the room. The big question is when will it launch?

And on that Microsoft is being wildly vague. It says its Hololens should be available “in the first half of 2017,” and it says it has already shared the specs for PCs that will power its new headset, with those PCs available “next year.” It says developer kits will be made available to developers at the Game Developers Conference in San Francisco in February.

And it announced that the hardware developer 3Glasses will “bring the Windows 10 experience to their S1 device in the first half of 2017” – but that’s not the same as saying Microsoft Evo headsets will be available by then.

Incidentally the minimum specs for the new headsets are:

• Intel Mobile Core i5 dual-core
• Intel HD Graphics 620 (GT2) or equivalent
• 8GB RAM
• HMDI 1.4 or 2
• 100GB drive (preferably solid state)
• Bluetooth 4.0

Taking all the announcements together, it looks as though Microsoft is aiming at a Q3 or Q4 2017 launch of its VR headset – a timeline that is likely to give Oculus, Vive and Sulon a few months’ head start, but probably not enough of one to steal the market.

Where Microsoft and Intel really could win, however, is if they do manage to create a good VR system that requires a less powerful machine to run. That would pull down the price tag for the whole system and place it above the current best offering on the market – the PlayStation VR – in terms of quality. ®

Sponsored:
Magic quadrant for enterprise mobility management suites