What’s Driving the Shift to Multicloud: AI/ML for Cloud AIOps

Purple Aibrain

By: R. Scott Raynovich

(This blog series is sponsored by Megaport. The content is independent content written by Futuriom analysts and reviewed by the sponsor.)

This is the third installment in a series of tech primers about what’s driving the move to multicloud. Part one covered cost containment; part two covered network security. This third piece covers how artificial intelligence (AI) and network-as-a-service (NaaS) presents a potent combination for network managers who’d like a more automated approach to provisioning today’s complex cloud requirements.

NaaS enables dynamic network services to be instantaneously provisioned with software, which is useful for connecting diverse network resources such as enterprise networks, datacenters, and cloud services. NaaS connectivity can include on-prem-to-cloud, cloud-to-cloud, and branch-to-cloud connectivity.

Now there’s the potential to inject NaaS with AI and machine learning (ML) to deliver AIOps for cloud operations – allowing the network and cloud to increasingly run itself.

Why is this important? In the expanding hybrid and multicloud world, networks are constantly changing. Frequently shifting applications and workload traffic mean that network connections need to be responsive to real-time needs. The expansion of available NaaS and cloud networking services means that is now possible.

How AIOps Plug into NaaS

The rise of public cloud infrastructure such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and a variety of edge infrastructure services such as content delivery networks (CDNs) is expanding the potential for NaaS. This web of available virtual networks, in combination with NaaS services such as Megaport, allow organizations to leverage third-party infrastructure to support network and cloud operations.

The next step is AIOps. AIOps, which is short for AI for operations, is a term to describe the increased automation of cloud and network orchestration. The rise of AI/ML has made it easier to automate networks by ingesting network data and telemetry and programming them to respond to potential scenarios and needs.

The use of AIOps and AI/ML is going to increase in the foreseeable future, as it makes difficult tasks such as planning network capacity and responding to threats or outages much easier.

How does AIOps work? Key elements of the technology include:

  • Gathering and analysis of data from networking systems using APIs
  • Use of statistical optimization algorithms with assistance of AI/ML to model and analyze data
  • Use of AI/ML to “learn” patterns and models based on historical and real- time data. These models can be used to predict traffic patterns or potential solutions to networking faults and problems
  • Some type of management dashboard to summarize network connectivity and performance, along with automation options.

Megaport ONE: How AIOps and NaaS Work Together

The rapid expansion of cloud networks, NaaS, and services such as cloud on-ramps means that in the future, organizations will be able to partner with third parties to more quickly build responsive cloud operations.

Some of the leading NaaS providers see this trend coming. For example, Megaport, a leading global NaaS provider, recently demonstrated how NaaS and AI/ML are coming together to produce AIOps within Megaport ONE, a service to help datacenter and managed service providers (MSPs) orchestrate all things cloud – high-performance compute, storage, and networking.

Megaport ONE services include the capability to use AI/ML to forecast capacity and then allow customers to schedule alerts, upgrade at the next maintenance window, or perform an immediate upgrade and receive usage and capacity planning notifications for CPUs and GPUs. It’s a multi-tenant software-as-a-service (SaaS) platform for datacenter operators and managed service providers to connect and control complex infrastructure and operations. Megaport ONE can manage everything from resource utilization statistics to latency, jitter, and packet loss dashboards and reporting. It includes support for Amazon Web Services (AWS), Microsoft Azure, Google Cloud, bare metal, and edge infrastructure.

Megaport’s recent moves show how public cloud infrastructure, NaaS, and AI/ML can be combined to provide powerful AIOps services to enable instantaneous provisioning of cloud infrastructure on demand.