Why companies should opt for cloud networking

The cloud offers an alternative model for consuming IT services in which companies move away from on-premises models and toward hosted services, where cloud providers offload IT operations on their behalf.

In this article, I will discuss some of the benefits, which include simplicity, resiliency, scalability, cost and data-driven solutions.

Traditionally, customers managed all the installation and operation of hardware, power, cabling, upgrades, backups, migrations and redundancy.

They did this for routers, switches, controllers, access points, management tools, gateways, concentrators and other services. They ensured proper connectivity and bandwidth, security and integration among all these systems.

Then they monitored, troubleshot, inventoried, managed support issues and planned lifecycle for all of it, end-to-end.

Across a large or distributed deployment, that is a lot of work. This is where cloud networking comes to the rescue.

The cloud eases the demands of traditional deployment models by shifting the burden of operation to the cloud provider.

The operation of all these services is largely transparent to the customer, and the cloud provider offloads the complexity and overhead, so IT groups can focus on solving business problems instead of hassling with servers.

Customers enjoy faster feature velocity and the latest functionality without the pain points of system migration, upgrades, surprising bugs, overnight maintenance windows, rollbacks and other headaches.

The cloud eases the demands of traditional deployment models by shifting the burden of operation to the cloud provider.

From an infrastructure perspective, it becomes someone else’s responsibility − namely the server and data centre specialists.

Cloud simplicity also improves daily workflows, such as logging in without needing a VPN, registering new devices, configuring and applying policies centrally, licence management and enjoying centralised visibility across distributed deployments.

Not only does the cloud provider simplify system management, but it will also typically make a guarantee to provide 99.99% (or more) uptime.

Cloud solutions are built on top of hyper-reliable infrastructure and platform architectures offered by trusted cloud operators like Amazon, Microsoft and Google.

Of course, there are many different versions of cloud architecture and not all clouds are created equal; but in general, the user experience of the cloud is highly reliable because the cloud has inherent resiliency against failure and redundancy in case of failure.

When hearing about scalability, you may think about vast infrastructure networks and you may tune out if you do not manage such a network.

Scale is not just about growing big; it is also about any changes that drive a network into new tiers of operation, like expanding or changing client device or user populations, new business requirements, spikes in events/alarms, adding locations/sites, new data visibility or reporting requirements, or simply logging in more frequently. Little stair steps in growth eventually add up and create scale pinch points.

The cloud replaces the fixed resourcing paradigm of on-premises hardware, and addresses scalability by using an elastic and flexible resource paradigm. Cloud resources can be closely monitored and integrated with automation tooling that can dynamically scale up and down as capacity requirements change.

Vendor solutions continually evolve based on new applications and customer requirements, but traditional solutions bind up that evolution with resource limitations.

There are always challenges with adding more boxes to scale. Even with virtual machines, the hardware resource pool may be exhausted, which might require a new budget cycle to justify additional spending.

In the cloud, these scale transitions are much easier to handle for network admins, and it is often transparent.

The cloud scales linearly, simply by adding licences and connecting devices. Cloud operators can also more easily manage system evolution because cloud services are modular, and scale limitations can be addressed either with additional resources or by architecture shifts to solve specific pain points.

By leveraging economies of scale, cloud providers have dramatically dropped the street price of compute, memory and disk resources, and made those lower prices available to users.

Costs are also easier to predict and manage because the cloud avoids those transitional growth stair steps, like when the existing appliance supports 1 000 devices, but the new design pushes it up to 1 025.

Cloud licences are typically sold in linear units as subscriptions, avoiding unexpected capex costs for infrastructure at known or unknown thresholds.

Perhaps the most important cost aspect is that the cloud enables customers to transition into operational expenditures instead of high upfront capital expenditures, which are common to on-premises solutions.

Beyond that, cloud services increasingly leverage on-demand computing models (for example, serverless) that streamline operational costs, which can be passed on to customers in several forms.

As customers become increasingly data-driven, and as machine learning (ML) and artificial intelligence (AI) automate networks, cloud solutions are outpacing on-premises options because the platforms enable faster development.

Cloud makes high-scale data processing and storage easier, and provides a more robust and modular toolset for data pipelines.

Cloud systems also make ML and AI more accessible because cloud toolkits are better for model training and maintenance, and of course, the training dataset is richer in a big data cloud architecture.

With all these benefits, cloud has become a fast favourite deployment model for networking solutions.

Cloud services are an on-demand model for computing, storage, security, networking and other IT resources that are provided as a service, hosted in remote data centres, accessed via the Internet, and paid by usage. 

A few common models drive the way cloud services are offered, and they are called as-a-service models. The table shows the three primary types of service models. You can think of them as progressive stages of readiness for a final cloud application.

* In my next article, I will discuss microservices, containers and orchestration.