Enterprises driving toward data-first modernization need to determine the optimal multicloud strategy, starting with which applications and data are best suited to migrate to cloud and what should remain in the core and at the edge.
A hybrid approach is clearly established as the optimal operating model of choice. A Flexera report found the shift to hybrid infrastructure supported by overwhelming numbers of survey respondents, with 89% of them opting for a multicloud strategy and 80% taking a hybrid approach that combines use of public as well as private clouds.
The shift toward hybrid IT has clear upsides, enabling organizations to choose the right solution for each task and workload, depending on criteria such as performance, security, compliance, and cost, among other factors. The challenge is that CIOs must apply a rigorous process and holistic assessment to determine the optimal data modernization strategy, given that there is no one-size-fits-all answer.
Many organizations set out on the modernization journey guided by the premise that cloud-first or cloud-only is the ultimate destination, only to find that the path is not appropriate for all data and workloads. “Directionally correct CIOs and the C-suite looked at the public cloud and liked the operating model: the pay-as-you-go, predefined services, the automation and orchestration, and the partner ecosystem all available to you,” says Rocco Lavista, worldwide vice president for HPE GreenLake sales and go-to-market. “Many tried to move their whole estate into public cloud, and what they found is that that doesn’t work for everything. It’s less about what application and data should go on public cloud and more about a continuum from the edge to core [in colocated or private data centers] to public cloud.”
Close to the Edge
There are several reasons why certain data and workloads need to remain at the edge, as opposed to transitioning to public cloud. Data gravity is perhaps the most significant arbiter of where to deploy workloads, particularly when there is a need to analyze massive amounts of data quickly — for example, with X-ray or MRI machines in a hospital setting, for quality assurance data from a manufacturing line, and even with data collected at point-of-sale systems in a retail setting.
Artificial intelligence (AI) projects are another useful example. “Where I’ve seen AI projects fail is in trying to bring the massive amounts of data from where it’s created to the training model [in some public cloud] and get timely insights, versus taking the model and bringing it closer to where the data is created,” Lavista explains. “Here, there is a synergistic need between what is happening at the edge and the processing power required in real time to facilitate your business objectives.”
Application entanglement presents another barrier keeping organizations from migrating some applications and data to cloud. Some legacy applications have been architected in a way that doesn’t allow pieces of functionality and data to be migrated to cloud easily; in other cases, making a wholesale migration is out of the question, for reasons related to cost and complexity. There are also workloads that don’t make economic sense to refactor from operating in a fixed environment to a variable cost-based architecture and others with specific regulatory or industry obligations tied to data sovereignty or privacy that prevent a holistic migration strategy in embrace of public cloud.
The HPE GreenLake Advantage
Given the importance of the edge in the data modernization strategy, HPE seeks to remove any uncertainty regarding where to deploy applications and data. The HPE GreenLake edge-to-cloud platform brings the desired cloud-based operating model and platform experience, but with consistent and secure data governance practices, starting at the edge and running all the way to public cloud. This can be applied across any industry — such as retail, banking, manufacturing, or healthcare — and regardless of where the workload resides.
HPE GreenLake with the managed service offering is inclusive of all public clouds, ensuring a consistent experience whether data and applications are deployed on AWS, Microsoft Azure, or Google Cloud Platform as part of a hybrid mix that encompasses cloud in concert with on-premises infrastructure in an internal data center or colocation facility.
“IT teams want a unified solution they can use to manage all technology needs, from infrastructure as a service (IaaS) to platform as a service (PaaS) and container as a service (CaaS), that drive automation and orchestration that are not snowflakes,” says Lavista. “HPE GreenLake provides that standard operating model from edge to core and all the way through to the public cloud.”
By aligning with HPE GreenLake solutions, IT organizations also free themselves of the day-to-day operations of running infrastructure to focus on delivering core capabilities for business users as well as DevOps teams. The HPE GreenLake team works with organizations to assess which workloads are a better fit for cloud or edge, by evaluating a variety of factors, including technical complexity, system dependencies, service-level agreement (SLA) requirements, and latency demands. For example, a quality control system on a manufacturing line might be better suited for an edge solution, due to the need to analyze data in volume and in near real time. But an AI application that could benefit from a facial recognition service might be better served by public cloud for such service, given the broad ecosystem of available third-party services that eliminate the need to re-create the wheel for every innovation.
To ensure top performance, Lavista counsels companies to fully understand their core business objectives and to be pragmatic about their cloud migration goals so they avoid the trap of moving data and workloads simply because it’s the latest technology trend. “Understand your options based on where you are coming from,” he says. “If what you are looking for is to optimize the IT operating model, you can still get that without moving applications and data.”
For more information, visit https://www.hpe.com/us/en/solutions/edge.html
