Cloud infrastructure, a service-oriented architecture and microservices are becoming integral to every enterprise digital transformation. In recent years, organizations have adopted hybrid and multi-cloud deployments as a key ingredient of their digital transformation strategy. This hybrid, multi-cloud cloud approach allows organizations to distribute technology infrastructure availability related risks, choose the right cloud for their specific workloads, be closer to their customers, and facilitate data exchange between public cloud and on-premises platforms. Despite these benefits, there are significant challenges in these models.
Before we begin, lets first define what exactly constitutes an hybrid multi-cloud environment.
What is the Hybrid Cloud?
With hybrid cloud, enterprises use a blend of on-premises, private cloud, and public cloud infrastructure:
- On-Premises – This is where the IT infrastructure is hosted and managed within the organization, such as in a server room within an office, or as a part of a self-owned data center or a co-lo facility within a larger data center.
- Public Cloud – This is where servers and IT infrastructure are in the cloud, with resources that can be rented or leased on different payment models. Usually, in a public cloud environment, the IT infrastructure resources are shared among various users, and hence are multi-tenant.
- Private Cloud – This is an isolated version of the public cloud, where the entire IT infrastructure is dedicated and private to one user. Resources are not shared, which offers greater stability, reliability and flexibility.
- Hybrid Cloud – In this model, IT infrastructure is a combination of Private Cloud, usually a businesses own data center, along with Public Cloud infrastructure utilized together to provide the businesses IT services.
- Hybrid, Multi–Cloud – Hybrid, Multi-Clouds are a conjunction of at least 2, or more public clouds being utilized in addition to the private datacenter, to provide the infrastructure required for the businesses’ IT services.
All these various models and platforms, look great to begin with. And the more the choices, the more we love, but the ensuing complexity overwhelms us, significantly. A lot of businesses are now reaching a point where there technology footprint is seeming unmanageable.
This is where challenges in the hybrid multicloud manifest, that is, connecting these various clouds together so they may function and can be utilized, seamlessly.
Prominent Business Challenges in the Hybrid, Multi-Cloud
- A lack of seamless workload portability between clouds. Here, portability is defined as the ability to move workloads and data between cloud platforms, with minimal-to-zero disruption to business operations.
This reflects how existing hybrid cloud solutions are limited and constrained. Underlying virtualization platforms, private or public, have limited support for workload mobility, i.e. limited to be within the same platform, let alone cross-platform. EEven if cross-platform solutions work, they have unreliable service level agreements (SLAs) that constrain businesses due to risk of disruption. - Recovery across hybrid cloud platforms is primarily driven by the originating cloud platform. This drives up operational complexity as organizations must manage multiple disparate tools, processes, and SLA agreements to maintain business continuity.
- Real-time data availability is what enables data-driven decision-making processes. There are solutions to get data to the right place, solutions to analyze the data once in the right place, and solutions to send analyzed data to stakeholders for review. The problem is that these solutions are de-coupled and not integrated, making “real time” an undefined period.
A Growing Need for Hybrid, Multi-Cloud Portability
If enterprises want to successfully adopt a hybrid, multi-cloud strategy, the challenges discussed must first be resolved. Organizations demand a “boundary-less” cloud without constraints, backed by a reliable recovery SLA that defines “real time” as a fixed time duration. If this fixed period is breached, it is no longer recovery-in-real-time.
This highlights the need to challenge and change the definition of the hybrid, multi-cloud computing model, beyond simply linking or connecting clouds together, and beyond just adding a wire between these clouds. Analyst firms agree that cloud workload portability is a necessity. For E.g. Gartner in its 2021 Hype Cycle Report has categorized ‘Cloud Workload Portability’ as a ‘Required Innovation Trigger’. Regulators, on the other hand, have started mandating a hybrid, multi-cloud model, i.e. not being dependent on a single cloud or service provider. Leading to the same need of seamlessly portability of workloads across and between private and public clouds.
To sum up existing hybrid, multi- cloud challenges in the context of regulatory action, this is what enterprises need to do and consider:
- Redefine “Workloads” – A workload needs to be viewed as one set—including OS, platform, applications, code, workload configurations, and data—to drive greater workload portability.
- Remove Boundaries – A boundary-less cloud environment is vital, often referred to as cloud-agnostic. This is where enterprises have complete freedom of choice without building dependencies on a specific platform or hypervisor technology.
- Consolidating Recovery – Regardless of the cloud source, platform, or size, enterprises should have the ability to consolidate recovery plans onto a single site.
- Consistent SLAs – A consistent, flat-lined recovery SLA is needed to mitigate any potential disruption when recovering workloads between clouds.
- Consistent SOP – A consistent, similar mechanism to replicate and recover entire workloads from any cloud, to any other cloud.
Drive Greater Hybrid Cloud Portability with Datamotive
Datamotive is a hybrid, multi-cloud replication and recovery platform that is focused on resolving these challenges and redefining the hybrid, multi-cloud computing model to align with enterprise needs. It does this by eliminating the boundaries between clouds and driving seamless workload portability across and between clouds.
Migrate, scale, and recover entire workloads as one unit—including OS, platform, applications, code, workload configurations, and data. Datamotive supports public and private clouds, with an industry-first 10-minute recovery SLA—guaranteed! The source cloud, target cloud, application, platform, and size of workload have no bearing on this guarantee.
Datamotive wants to help organizations develop a cloud strategy led by workloads, rather than the underlying cloud platforms, thus enabling enterprises to choose the right place, size, and price for each workload, without being limited by the cloud platform or their dependencies.
What if you could move across clouds with a 10-minute recovery SLA, without losing existing regulatory or compliance configurations? Additionally, there is no need to stream or hydrate data in alignment with hypervisor requirements, giving enterprises the freedom to:
- Enable services on the cloud platform that best caters to their customers. Timescales of weeks or months are reduced to 10 minutes, helping enterprises rapidly scale their workloads across clouds.
- Consolidate recovery plans onto a single site, within a single process, and one 10-minute recovery SLA. This greatly reduces complexity, meaning Datamotive can reduce the total cost of ownership (TCO) by 60% irrespective of source cloud, platform, or workload size.
- Datamotive disaster recovery (DR) offers regulatory compliance as-standard. This includes PCI-DSS and HIPAA configurations that are fully retained during the recovery process, without the need for lines of code or additional effort.
Does a boundary-less hybrid cloud computing framework that aligns workloads with consumer need, that not only simplifies, but also enhances your business? You can experience the redefined hybrid, multi-cloud with Datamotive.
Eliminate these cloud boundaries and accelerates your digital transformation and business growth. Request a demo of Datamotive today!
(The author is Mr. Yogesh Anyapanawar, Founder and CEO, Datamotive and the views expressed in this article are his own)
