April 30, 2020 AIOps Makes Automated Hybrid IT Environments Possible

What is AIOps? Will it really transform IT and if so, how?

By: Scott Leatherman, CMO, Virtana

Considering that IT infrastructure is essentially just a collection of smart machines, you’d think that automating operations would have happened a long time ago. So why is AIOps only just now rocking the IT world? I think the answer lies in the explosion of machine learning and artificial intelligence.

But first, what is AIOps? Will it really transform IT and if so, how?

AIOps is artificial intelligence for IT operations and yes, it stands to transform IT and operations massively. The deployment of AIOps lets infrastructure administrators use a vast array of real-time data, algorithmic insight, and machine learning to get the very best from optimizing on private and public clouds – all while automating the way companies migrate applications and workloads to the cloud or to next generation platforms.

Let’s dive into the ways AIOps can be achieved and seamlessly integrated into all forms and combinations of data centers.

Clouds, Data Centers, and Automated Orchestration

The past few years have seen mass migration to the cloud, and it’s now common for an organization to use cloud storage and cloud-based applications daily. Much of this data is held and managed in the public cloud (Azure, GCP, AWS, etc) and PaaS and SaaS providers. The public cloud is typically coupled with on-premises infrastructure, being managed by in-house IT teams, third-party providers (System Integrators/MSP’s, etc) and colocation hosting providers. Hybrid clouds, which make up this mix of both on-premises and cloud arrangements are increasingly the way enterprise organizations consume IT.

The benefits of moving to the cloud are well understood: business agility, massive scale, ultimate flexibility, and organizations not having to spend huge CapEx and OpEx to run their own data centers.

However, the widespread adoption of the cloud and resulting hybrid cloud environments has generated a highly complex IT landscape that now requires infrastructure owners to integrate, monitor, and maintain multiple applications, infrastructures, and locations simultaneously. Machine learning and AI algorithms can now permit the automation of many of these routine tasks. But the management of this technology must also be incorporated into the organization’s IT domain, and this challenge will grow as the use of AI expands.

As the options for hybrid/public/on-premises data centers have proliferated, organizations have moved between them according to specific business needs. This means that cloud migration is not a one-time event. We read about significant reassessment of cloud consumption due to high costs, performance issues, or simply the migration of a very complex infrastructure to cloud/colocation being too much of a heavy lift. As the cost models improve, technology such as containers make migration easier, and with the increased agility the cloud provides, we will see continued migration to and from the cloud for the foreseeable future.

For IT teams, multiple clouds and migrations result in a plethora of management paradigms that are extremely difficult to manage, let alone optimize. There are diverse hosts and/or applications being managed through a wide range of tools and accessed through different dashboards. These lack natural synergies – and more importantly – context. For example, if there’s an outage in one silo and an application performance issue in the cloud, the fact that the individual tools can’t speak to each other requires a lot of manual intervention to try and root cause the problems. This is true shame – because, if those synergies could be realized and an overview of the entire IT infrastructure and its functioning from a single viewpoint was presented, the business impact would be huge: reduced outages, increased productivity, and the resulting positive revenue impact.

Fortunately, such synergies are possible to capitalize on – through AIOps – and the potential is indeed vast.

Intelligent Ops – Artificial and Real

The evolution of IT, particularly in the last few years, has resembled that of the automobile. In its early days, the car was managed, driven, and maintained by the owner, with input from a third-party specialist. Today, the car is driven by its owner, and still has attention from the specialist, but much of the process is augmented by an on-board computer that keeps it running and diagnoses faults.

AIOps gives infrastructure owners capabilities comparable with those of specialists working on modern cars who rely on tooling and diagnostics to highlight the problem; the machine augments the technician, providing insight across all tiers of infrastructure, regardless of location or data center type, via a single interface.

Using AIOps this way provides two key benefits:

1) The ability to see all applications and functionality in real time, with context – allowing the organization to pre-empt outages and issues that the AIOps algorithms detect.

2) To use AIOps real-time analytics to optimize choices around operations and infrastructure and which application and workloads should be moved to the cloud based on algorithmic insight and reliable predictions of future consumption.

The beauty of AIOps lies in its ability to cut through the ‘noise’ generated by the many moving parts of modern IT infrastructure and clearly show what’s working and what’s not (or may not, in the future). This gives IT teams the power to predict and avoid outages based on historical data, to expedite and guarantee successful cloud migrations, and to make real-time decisions around workload and application placement. This, in turn, lets the organization get the best from cloud capability, maximize data center value, and optimize infrastructure resource/capital spend.

Understandably, some are reluctant to throw an entire business behind this new concept right away – although the signs suggest that AIOps, like driverless cars, will eventually become the new normal. But a gradual introduction is in any case perfectly feasible — this capability can be applied across the board or used with a few initial applications and then scaled up in ‘baby steps’, according to business objectives.

AIOps is an almost inevitable revolution; the next step in infrastructural visibility, awareness, and control. The capabilities of AIOps have the potential to make IT managers’ jobs easier, more efficient and – hopefully – more appreciated.

www.virtana.com

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