Welcome!

@CloudExpo Authors: Liz McMillan, Zakia Bouachraoui, Yeshim Deniz, Pat Romanski, Elizabeth White

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Containers Expo Blog, Apache, Cloud Security

@CloudExpo: Blog Feed Post

Disaster Recovery Ascends to the Cloud | Part 2: Deployment Considerations

Realizing an economical alternative to traditional DR

As mentioned in Part I of this series, cloud technology has introduced a viable alternative to the practice of creating secondary sites for disaster recovery (DR), promising to save IT organizations hundreds of thousands or even millions of dollars in infrastructure and maintenance. While the cost reduction associated with replacing dedicated DR infrastructure is intuitive, the ability of cloud solutions to meet the recovery times (RTOs and RPOs) dictated by businesses is often less well understood.

Part I suggested two key considerations in recovering IT operations from a disaster are (1) regaining access to data and (2) regaining access to applications. Today’s cloud integrated storage or cloud storage gateways can push backups or live data sets to the cloud easily and securely, enabling the first element of a cloud DR solution. With this in mind, let’s examine two strategies for application recovery using cloud-based DR:

Strategy 1: Data copies in-cloud, application recovery off-cloud
One of the simpler approaches to cloud-based DR stores data copies in the cloud and allows external, off-cloud access by applications in the case of a primary site outage. With data in the cloud accessible from nearly anywhere, applications may be recovered at a secondary site if they cannot be recovered at the primary site.

The advantage of this approach is the elimination of dedicated secondary storage infrastructure for DR. The disadvantage is the requirement for a secondary site for application recovery.

An improvement to this approach involves leveraging a hosting provider as the application recovery site, where new application servers can be provisioned on-demand in case of a disaster. Using a hosted recovery site can be considerably faster than restoring and rebuilding the original application environment and more economical than maintaining a dedicated secondary site. However, recovery times may be impacted by the time it takes for the hosting provider to provision new servers.

Application recovery off-cloud versus in-cloud

Strategy 2: Data copies in-cloud, application recovery in-cloud
Perhaps a more ideal approach to cloud-based DR enables both data and application recovery in the cloud without the need for a secondary site for applications or storage. Cloud compute as-a-service represents an attractive environment for recovering applications by rapidly spinning up new virtual servers.

When using a cloud storage gateway to replicate data to the cloud, consider cloud gateways with the ability to run in the cloud. Cloud servers can then attach to the gateway to facilitate application recovery.

The process of application recovery may involve activating servers and applications via a cloud provider’s catalog. Although this process is much faster than provisioning new physical hardware, it can still be time consuming, particularly when attempting to recover tens or hundreds of servers.

Alternatively, virtual machines that resided on-premise can be reinstantiated in the cloud, similar to failover of virtual machines between hypervisors. This is possible if the same hypervisor runs on-premise and in the cloud. However, while moving virtual machine (VM) images between like hypervisors is generally straightforward, many cloud providers may not offer sufficient administrative privilege in their virtual compute environments or may not be compatible with on-premise hypervisors.

To get around these limitations and incompatibilities, an emerging option involves importing on-premise VMs into the cloud via conversion scripts and tools. An important consideration is ensuring that these conversion scripts and tools operate bidirectionally, meaning they allow a way to eventually export VMs back to the on-premise environment.

The keys to success are testing and working with a partner you trust
While there are a variety of ways to deploy DR in the cloud, there are many subtleties and details to consider. Not surprisingly, the devil is often in the details.

Keep in mind that an important aspect of any DR strategy is conducting regular testing and validation. Additionally, working with technology partners who understand the advantages and tradeoffs of DR in the cloud can be particularly helpful.

Like any major IT undertaking, DR in the cloud requires significant planning — but the payoff can be substantial if reducing disaster recovery costs and improving availability are important to your business.

Read the original blog entry...

More Stories By Nicos Vekiarides

Nicos Vekiarides is the Chief Executive Officer & Co-Founder of TwinStrata. He has spent over 20 years in enterprise data storage, both as a business manager and as an entrepreneur and founder in startup companies.

Prior to TwinStrata, he served as VP of Product Strategy and Technology at Incipient, Inc., where he helped deliver the industry's first storage virtualization solution embedded in a switch. Prior to Incipient, he was General Manager of the storage virtualization business at Hewlett-Packard. Vekiarides came to HP with the acquisition of StorageApps where he was the founding VP of Engineering. At StorageApps, he built a team that brought to market the industry's first storage virtualization appliance. Prior to StorageApps, he spent a number of years in the data storage industry working at Sun Microsystems and Encore Computer. At Encore, he architected and delivered Encore Computer's SP data replication products that were a key factor in the acquisition of Encore's storage division by Sun Microsystems.

CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.