You don’t need to be at the cliff face to embrace edge computing
Where applications and data run is critical for many reasons. Not just for fast access to data to capitalise on the business promise of real-time (or near real-time) AI and the evolution of Web3 but also to ensure better security and governance.
That is something we can all agree on. But the evolution of the edge and the corresponding intelligent edge creates a new problem. Where does an organisation host their edge? Is it a private, public, hybrid or multi-cloud environment? Let's consider that Garner has identified Hyperscale Edge Computing as a critical enabler in its 2023 Gartner Emerging Technologies and Trends Impact Radar but has given it a 3-8 year range to mature. It's essential to understand how those companies with a hybrid multi-cloud environment are already succeeding.
It is clear that centralised hyperscalers aren't the flavour of the month when it comes to the edge. The pendulum is swinging away from them and has been since 2022. The reason is that edge computing is generally employed across a network of data centres to allow high-performance workloads to sit closer to the end user.
Creating consistency is key
In a 2020 poll by Statista, it showed that 69% of companies using hybrid clouds had adopted hyperconverged infrastructure (HCI) platforms to help make mixed-cloud management easier. This is important as the application of HCI has now extended beyond just the centralisation of data centres to include edge computing.
The reason for this is that HCI offers better provisioning, monitoring, management, and on-demand scaling, all of which are challenges when managing a diverse edge. HCI's superpower is that it supports hardware component integration and can centralise the collection and generation of data at the edge and across multiple edge or remote office and branch office (ROBO) sites.
Because of its virtualised and software-defined nature, HCI enables a customer to remotely implement a unified view and management layer to edge devices. This immediately strips away error-prone manual configuration of separate hardware appliances, no matter where they are hosted. Further, it allows the user to host the management layer wherever they need to, on- or off-premises, and then manage this through a single consistent cloud operating model via a single platform.
Critical edge enablers
A few factors drive the need for a more seamless and manageable hyperscale edge. There is now a highly distributed remote workforce, multiple data stores, and a growing number of edge devices, all of which need support. It goes without saying that local infrastructure that sits closer to the user (or device) strips away the challenge of distance-induced latency regardless of whether your IT team needs to support a thick (enterprise edge) or thin (and IoT) edge.
The real-time nature of applications that rely on sensors, cameras, processing, and storage in edge locations is another factor driving the hyperscale edge. An edge data centre will be called on to support, collate, and collect streaming data to deliver IoT and artificial intelligence (AI)-driven analytics that is the cornerstone of the automation of edge-centric applications.
One key consideration of the edge data centre is whether it is autonomous or if it connects to other sites and shares resources with other areas of the business. The intelligent edge needs decisions to be made where the user is, whereas other edge deployments are just there to collect or generate data for analytics to be performed later. Regardless, businesses must seamlessly integrate their edge resources to deliver IT support and manage these from anywhere.
Connecting the edges
As it stands, several companies are dealing with islands of infrastructure with server, storage, network, hypervisor, container, and backup resources from different vendors, all managed separately. Added to this are the multiple workloads and applications they have, again, across multiple clouds and sites. This model doesn't scale.
It's crucial for customers managing multiple locations (and clouds) as they can now perform upgrades, deliver security and data protection, and add applications across multiple environments from a centralised place. This is where the hyperscale edge comes into play. A user can take advantage of simplicity, flexibility, and rapid scalability by consolidating infrastructure and application management on a single management platform with an HCI-based approach.
With this approach, an organisation can also better manage their storage costs. Critically, HCI platforms that unify object, file and block storage under the management and same software-defined infrastructure as their other workloads help simplify and lower the cost of storing data.
A hyperscale edge
In short, hyperscale edge computing delivers the higher compute capability needed to handle more complex data requirements and analytics, allowing these actions to run closer to the point of data generation. The end result is real-time intelligence and insight.
The best approach is to start treating remote sites as an extension of the data centre. When using a single cloud/edge management platform, a business can integrate data management, backup and storage from multiple sites and in different formats. This highly scalable model can extend a consistent IT experience across private, hosted/managed, or public clouds and the edge.
And the great thing is that we do not need to wait for this. It is all possible today with a hybrid multi-cloud platform.